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NUMERICAL AND EXPERIMENTAL INVESTIGATIONS
OF THE MACHINABILITY OF Ti6Al4V
Energy Efficiency and Sustainable Cooling/ Lubrication Strategies
Salman Pervaiz Doctoral Thesis
KTH Royal Institute of Technology Department of Production Engineering
Stockholm, Sweden October 2015
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TRITA-IIP-15-07ISSN 1650-1888ISBN 978-91-7595-702-9
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ABSTRACT
Titanium alloys are widely utilized in the aerospace, biomedical, marine, petro-chemical and other demanding industries due to their durability, high fatigue resistance and ability to sustain elevate operating temperature. As titanium alloys are difficult to machine, due to which machining of these alloys ends up with higher environmental burden. The industry is now embracing the sustainable philosophy in order to reduce their carbon footprint. This means that the best sustainable practices have to be used in machining of titanium alloys as well as in an effort to reduce the carbon footprint and greenhouse gas (GHG) emissions.
In this thesis, a better understanding towards the feasibility of shifting from conventional (dry and flood) cooling techniques to the vegetable oil based minimum quantity cooling lubrication (MQCL) was established. Machining performance of MQCL cooling strategies was encouraging as in most cases the tool life was found close to flood strategy or sometimes even better. The study revealed that the influence of the MQCL (Internal) application method on overall machining performance was more evident at higher cutting speeds. In addition to the experimental machinability investigations, Finite Element Modeling (FEM) and Computational Fluid Dynamic (CFD) Modeling was also employed to prediction of energy consumed in machining and cutting temperature distribution on the cutting tool. All numerical results were found in close agreement to the experimental data. The contribution of the thesis should be of interest to those who work in the areas of sustainable machining.
Keywords: Titanium alloys, Energy consumption, Wear mechanisms, Finite element analysis, computational fluid dynamic analysis,
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PREFACE
This thesis is based on the work performed under a collaborative project between Production Engineering Department, Royal Institute of Technology and Mechanical Engineering Department, American University of Sharjah. The presented work has been conducted during the years 2011-2015 at the Manufacturing Laboratory, Department of Mechanical Engineering, American University of Sharjah, UAE.
This thesis is mainly based on the following papers numbered 1 to 10 as provided in the list of papers. Part of the results presented in the thesis is also taken from the test performed for the Master Thesis work by Syed Waqar Raza.
Regarding author’s contribution to the papers, where nothing else is stated Salman Pervaiz is the main author and rest of the co-authors have been provided guidance and reviewed the papers and proposed amendments.
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LIST OF PAPERS FOCUSE AREA 1: MACHINABILITY
Paper I: Pervaiz, S., Rashid, A., Deiab, I., and Nicolescu, M. "Influence of tool materials and tool wear on machinability of high performance alloys: A review" Materials and Manufacturing Processes Vol. 29, pp. 219 - 252, 2014.
Paper II: Raza, S.W., Pervaiz, S., and Deiab, I. “Tool wear patterns when turning of titanium alloy using sustainable lubrication strategies”, International Journal of Precision Engineering and Manufacturing, , Volume 15, Issue 9, pp 1979-1985, 2014.
Paper III: Deiab, I., Raza, S.W., Pervaiz, S. “Analysis of lubrication strategies for sustainable machining,” Procedia CIRP (Elsevier) Volume 17, 2014, Pages 766–771.
Paper IV: Pervaiz, S., Deiab, I., Rashid, A., and Nicolescu, M. “Minimal Quantity Cooling Lubrication (MQCL) in Turning of Ti6Al4V: Influence on Surface roughness, Cutting force and Tool Wear” Accepted in Proceedings of the Institution of Mechanical Engineers, Part B: Journal of Engineering Manufacture.
Paper V: Pervaiz, S., Deiab, I., Rashid, A., and Nicolescu, M. “Machinability of Ti6Al4V using minimum quantity cooling lubrication under various oil supply rates,” Submitted in International Journal of Advanced Manufacturing Technology.
FOCUSE AREA 2: ENERGY CONSUMPTION USING FEA Paper VI: Pervaiz, S., Deiab, I., Rashid, A., and Nicolescu, M. “A finite element modelling approach to predict energy consumption and environmental implications for machining operation” Accepted Proceedings of the
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Institution of Mechanical Engineers, Part B: Journal of Engineering Manufacture. First published on July 25, 2014 as doi:10.1177/0954405414541105
Paper VII: Pervaiz, S., Deiab, I., Rashid, A., and Nicolescu, M. “Experimental and Numerical Investigation of Ti6Al4V Alloy machinability using TiAlN Coated Tools” Transactions of the SME , 42th North American Manufacturing Research Conference (NAMRC42) , University of Michigan, Detroit, Michigan, United States, June 9-13 2014.
FOCUSE AREA 3: COMPUTATIONAL FLUID DYNAMIC MODELING
Paper VIII: Pervaiz, S., Deiab, I., Wahba, E., Rashid, A., and Nicolescu, M., “A coupled FE and CFD approach to predict the cutting tool temperature profile in machining,” Procedia CIRP (Elsevier) Volume 17, 2014, Pages 750–754.
Paper IX: Pervaiz, S., Deiab, I., Wahba, E., Rashid, A., and Nicolescu, M., “A novel numerical modeling approach to determine the temperature distribution in the cutting tool using conjugate heat transfer (CHT) analysis,” Accepted in International Journal of Advanced Manufacturing Technology. DOI: 10.1007/s00170-015-7086-2
Paper X: Pervaiz, S., Deiab, I., Wahba, E., Rashid, A., and Nicolescu, M., “FE based simulation coupled with CFD analysis to investigate optimal value of convective heat transfer coefficient in machining”, Submitted in International Journal of Machine Tool & Manufacture. LICENTIATE THESIS: Pervaiz, S., "Investigating cooling and lubrication strategies for sustainable machining of titanium alloys: Impact on machinability and environmental performance", KTH Royal Institute of Technology - Production Engineering, Licenciate Thesis, TRITA-IIP-14-02, ISSN 1650-1888, ISBN 978-91-7595-091-4
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APPENDED PAPERS Due to the space limitation only below papers are appended in this thesis; Paper I: Pervaiz, S., Rashid, A., Deiab, I., and Nicolescu, M. "Influence of tool materials and tool wear on machinability of high performance alloys: A review" Materials and Manufacturing Processes Vol. 29, pp. 219 - 252, 2014.
Paper II: Raza, S.W., Pervaiz, S., and Deiab, I. “Tool wear patterns when turning of titanium alloy using sustainable lubrication strategies”, International Journal of Precision Engineering and Manufacturing, , Volume 15, Issue 9, pp 1979-1985, 2014.
Paper IV: Pervaiz, S., Deiab, I., Rashid, A., and Nicolescu, M. “Minimal Quantity Cooling Lubrication (MQCL) in Turning of Ti6Al4V: Influence on Surface roughness, Cutting force and Tool Wear” Submitted in Proceedings of the Institution of Mechanical Engineers, Part B: Journal of Engineering Manufacture.
Paper V: Pervaiz, S., Rashid, A., Deiab, I., and Nicolescu, M. “Machinability of Ti6Al4V using minimum quantity cooling lubrication under various oil supply rates,” Submitted in International Journal of Advanced Manufacturing Technology.
Paper VI: Pervaiz, S., Deiab, I., Rashid, A., and Nicolescu, M. “A finite element modelling approach to predict energy consumption and environmental implications for machining operation” Accepted Proceedings of the Institution of Mechanical Engineers, Part B: Journal of Engineering Manufacture. First published on July 25, 2014 as doi:10.1177/0954405414541105
Paper IX: Pervaiz, S., Deiab, I., Wahba, E., Rashid, A., and Nicolescu, M., “A novel numerical modeling approach to determine the temperature
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distribution in the cutting tool using conjugate heat transfer (CHT) analysis,” Accepted in International Journal of Advanced Manufacturing Technology. DOI: 10.1007/s00170-015-7086-2
UNAPPENDED PAPERS
The results of unappended papers are also presented in the thesis.
Paper III: Deiab, I., Raza, S.W., Pervaiz, S. “Analysis of lubrication strategies for sustainable machining,” Procedia CIRP (Elsevier) Volume 17, 2014, Pages 766–771.
Paper VII: Pervaiz, S., Deiab, I., Rashid, A., and Nicolescu, M. “Experimental and Numerical Investigation of Ti6Al4V Alloy machinability using TiAlN Coated Tools” Transactions of the SME , 42th North American Manufacturing Research Conference (NAMRC42) , University of Michigan, Detroit, Michigan, United States, June 9-13 2014.
Paper VIII: Pervaiz, S., Deiab, I., Wahba, E., Rashid, A., and Nicolescu, M., “A coupled FE and CFD approach to predict the cutting tool temperature profile in machining,” Procedia CIRP (Elsevier) Volume 17, 2014, Pages 750–754.
Paper X: Pervaiz, S., Deiab, I., Wahba, E., Rashid, A., and Nicolescu, M., “FE based simulation coupled with CFD analysis to investigate optimal value of convective heat transfer coefficient in machining”, Submitted in International Journal of Machine Tool & Manufacture. Salman Pervaiz Stockholm, October 2015
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ACKNOWLEDGEMENT The research study was supervised by Dr. Ibrahim Deiab representing University of Guelph, previously affiliated with the American University of Sharjah, and Dr. Amir Rashid and Professor Cornel Mihai Nicolescu representing Royal Institute of Technology. I would like to express my gratitude towards my all three supervisors for their valuable efforts, continuous support and professional mentoring throughout the research work.
I would like to acknowledge Mr. Recardo De Jesus, Senior Manufacturing Lab Tech, for his assistance in setting up the experimental setups.
I would like to thank Mr. Milan Martinovic (Accu – Svenska AB) for the generous support by providing the MQCL booster system for the study.
I would also like to thank Dr. Essam Wahba (American University of Sharjah) for providing very valuable guidance to develop Computational Fluid Dynamics model.
I am also deeply thankful to the Emirates Foundation for providing the required funding to carry out this research work.
Finally, I would also like to thank my family and friends for their continuous support.
Salman Pervaiz Stockholm, October 2015
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TABLE OF CONTENTS
ABSTRACT III
PREFACE IV
ACKNOWLEDGEMENT IX
TABLE OF CONTENTS X
LIST OF FIGURES XIII
LIST OF TABLES XIII
LIST OF ABBREVIATIONS XVIII
LIST OF NOMENCLATURE XIX
CHAPTER 1 : INTRODUCTION 2 1.1 Background 2
1.2 Thesis Scope and Aim 4
1.3 Research Problems 5
1.4 Scientific Contribution Summary of Appended Papers 5
1.5 Thesis Structure 8
References 9
CHAPTER 2 : STATE OF THE ART IN SUSTAINABLE MACHINING OF TITANIUM ALLOYS 11
2.1 Environment Friendly Machining Concepts 11
2.2 Machinability of Titanium Alloys 12
2.3 Heat Partitioning when Machining Ti6Al4V 16
2.4 Environmental Issues in Metal Cutting Sector 18
2.4.1 Proposed Sustainable Solutions for the Metal Cutting Sector 20
2.5 Advances in FEA Modeling for Machining Ti6Al4V 32
2.6 Utilization of Computational Fluid Dynamics (CFD) Modeling in Machining 37
2.7 Research Gaps from Literature Review 39
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References 40
CHAPTER 3 : EXPERIMENTAL METHODOLOGY 48 3.1 Design of Experiments (DOE) 48
3.2 Planning of Experiments 49
3.2.1 Workpiece and Cutting Tool Materials 50
3.3 Experimental Setups 52
3.4 Description of Vegetable Oil Based Minimum Quantity Cooling Lubrication (MQCL) System 55
3.5 Collection of Machining Variables 56
3.5.1 Measurement of Cutting Force 56
3.5.2 Measurement of Tool Wear and Wear Mechanisms 58
3.5.3 Measurement of Surface Roughness 60
3.5.4 Measurement of Cutting Temperature 61
3.5.5 MEASUREMENT OF CUTTING POWER 62
References 62
CHAPTER 4 : NUMERICAL METHODOLOGY 64 4.1 Finite Element Machining Simulations 64
4.1.1 Introduction 64
4.1.2 Modeling of the Cutting Tool Geometry 65
4.1.3 Modeling of the Workpiece Material 66
4.2 Computational Fluid Dynamic Simulations 70
4.2.1. Pre-Processing: Turbulence Model Selection 70
4.2.2. Computational Domain and Boundary Conditions 70
4.2.3. Meshing of Domains 73
4.2.4. Solver Execution 74
References 75
CHAPTER 5: RESULTS AND DISCUSSION FROM EXPERIMENTAL STUDIES (PAPERS I - V) 78
5.1 Outcomes of Literature Review (PAPER I) 78
5.2 Experimental Results (Papers II – V) 80
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5.2.1 Comparison between Internal and External MQCL 80
5.2.2 Comparison between different flowrates in Internal MQCL 92
5.2.3 Comparison between different lubrication techniques 104
References 110
CHAPTER 6: RESULTS AND DISCUSSION FROM NUMERICAL STUDIES (PAPERS VI - X) 112
6.1 Finite Element Machining Simulation (Papers VI – VII) 112
6.1.1 Simulated and Experimental Cutting Forces 113
6.1.2 Simulated and Experimental Energy Consumption 115
6.1.3 Estimation of CO2 Emissions 118
6.2 Computational Fluid Dynamic Simulation (PAPERS VIII – X) 118
6.2.1 Phase 1: Cutting Temperature Predictions Using FEA 118
6.2.2 Phase 2: Cutting Temperature Visualization on the Tool 120
6.2.3 FE and CFD based Coupling Procedure 124
Reference 131
CONCLUSIONS AND FUTURE WORK 132
Recommendations for future research work 135
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LIST OF FIGURES Figure 1:1 Structure of the thesis ........................................................................... 7 Figure 2:1: Schematic illustration of segmented chip formation [18] .. 16 Figure 2:2 Heat generation zones during the machining operation [20] ............................................................................................................................................ 16 Figure 2:3 Comparison of thermal loading for machining titanium and steel [9 -10] ................................................................................................................... 17 Figure 2:4 (a) Machine tools selected (b) Description of power consumption (c) Average energy breakdown of the reviewed machine tools [22] ........................................................................................................................ 19 Figure 2:5 Energy consumption breakdown for milling process [97] . 20 Figure 2:6Characterization of power consumption during cutting processes [25] .............................................................................................................. 21 Figure 2:7 Frictional shear and normal stress distribution over the rake face of tool [53] ............................................................................................................ 35 Figure 3:1Mircona tool holder to support internal delivery of MQCL system, (a) MQCL system, (b) Tool Holder, (c) Illustration of internal coolant delivery passage, (d) passage dimensions [4] ................................ 55 Figure 3:2Cutting force data evaluation system, (a) Kistler 9257b Dynamometer, and (b) Kistler 5070 charge amplifier ............................... 57 Figure 3:3Mitutoyo tool maker microscope (Model: TM 510) ................ 58 Figure 3:4(a) Scanning electron microscope (TEKSCAN), (b) Sample SEM images of uncoated carbide tool under Dry, Vc = 120 m/min and f = 0.15 mm/ rev ............................................................................................................ 59 Figure 3:5 Mitutoyo surface roughness tester SJ 201P ............................... 60 Figure 3:6 Schematic CAD illustration of calibration setup for thermocouples ............................................................................................................ 61 Figure 4:1 (a) CAD model TCMT 16 T3 04-KM H13A,and (b) CAD model CCMT 12 04 04 MM 1105 [1] ................................................................................. 65 Figure 4:2 Flow stress curves using modified Johnson-Cook constitutive model [3] .............................................................................................. 67 Figure 4:3 Remeshing as the cutting tool proceeds through the workpiece material (a) Step1 (b) Step 51 (c) Step 300 .............................. 69 Figure 4:4 Illustration of computational domain ......................................... 71 Figure 4:5 Stream lines of air at inlet velocity 0.1 m/sec interacting with the heat source at cutting tool tip.............................................................. 72 Figure 4:6 Computational domain for CFD model and meshing performed using CFX (ANSYS) mesh generator [13] ................................... 73
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Figure 4:7 CFX (ANSYS) Solver GUI for convergence achieved during our CFD simulations with residual target of 1.0e-6 ..................................... 74 Figure 5:1 (a) Surface roughness observed under dry, MQCL (Internal), MQCL (External) and flood cooling strategies, (b) Comparison of cutting force with respect to feed ........................................................................ 81 Figure 5:2 Flank wear measurement at cutting speed of 90 m/ min, (a) feed = 0.15 mm/ rev, (b) feed = 0.20 mm/ rev and (c) feed = 0.25 mm/ rev ..................................................................................................................................... 82 Figure 5:3 SEM images of the tool wear at cutting speed of 90 m/min and feed of 0.15 mm/ rev ........................................................................................ 83 Figure 5:4 (a) Surface roughness observed under dry, MQCL (Internal), MQCL (External) and flood cooling strategies, (b) Comparison of cutting force with respect to feed ........................................................................ 84 Figure 5:5 Flank wear measurement at cutting speed of 120 m/ min, (a) feed = 0.15 mm/ rev, (b) feed = 0.20 mm/ rev and (c) feed = 0.25 mm/ rev .......................................................................................................................... 85 Figure 5:6 SEM images of the tool wear at cutting speed of 120 m/min and feed of 0.15 mm/ rev ........................................................................................ 86 Figure 5:7 (a) Surface roughness observed under dry, MQCL (Internal), MQCL (External) and flood cooling strategies, (b) Comparison of cutting force with respect to feed ........................................................................ 88 Figure 5:8 Flank wear measurement at cutting speed of 150 m/ min, (a) feed = 0.15 mm/ rev, (b) feed = 0.20 mm/ rev and (c) feed = 0.25 mm/ rev .......................................................................................................................... 89 Figure 5:9 SEM images of the tool wear at cutting speed of 150 m/min and feed of 0.15 mm/ rev ........................................................................................ 90 Figure 5:10 Surface roughness results at feed levels from 0.1 – 0.3 mm/ rev and Cutting speed = 90 m/ min .................................................................... 92 Figure 5:11 Comparison of cutting force under different cooling strategies at cutting speed of 90 m/ min ......................................................... 93 Figure 5:12 Flank wear measurement at cutting Speed 90 m/ min (a) feed of 0.1 mm/ rev (a) (b) feed of 0.2 mm/ rev, (c) feed of 0.3 mm/ rev ............................................................................................................................................ 94 Figure 5:13 Surface roughness results at feed levels from 0.1 – 0.3 mm/ rev and Cutting speed = 120 m/ min .................................................................. 95 Figure 5:14 Comparison of cutting force under different cooling strategies at cutting speed of 120 m/ min ...................................................... 96 Figure 5:15 Flank wear measurement at cutting Speed 120 m/ min (a) feed of 0.1 mm/ rev (a) (b) feed of 0.2 mm/ rev, (c) feed of 0.3 mm/ rev ............................................................................................................................................ 97
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Figure 5:16 Wear mechanisms at feed 0.1 mm/ rev and cutting speed of 120 m/ min .............................................................................................................. 98 Figure 5:17 Wear mechanisms at feed 0.3 mm/ rev and cutting speed of 120 m/ min ................................................................................................................. 100 Figure 5:18 Surface roughness results at feed levels from 0.1 – 0.3 mm/ rev and Cutting speed = 150 m/ min ................................................................ 101 Figure 5:19 Comparison of cutting force under different cooling strategies at cutting speed of 150 m/ min .................................................... 102 Figure 5:20 Flank wear measurement at cutting Speed 150 m/ min (a) feed of 0.1 mm/ rev (a) (b) feed of 0.2 mm/ rev, (c) feed of 0.3 mm/ rev .......................................................................................................................................... 103 Figure 5:21 Surface roughness with respect to different lubrication techniques, where v1 = 90m/min, v2 =120m/ min, f1 = 0.1mm/rev and f2= 0.2mm/rev .......................................................................................................... 104 Figure 5:22 Flank tool wear with respect to different lubrication techniques, where v1 = 90m/min, v2 =120m/ min, f1 = 0.1mm/rev and f2= 0.2mm/ rev ........................................................................................................ 105 Figure 5:23 SEM micrograph of f = 0.1 mm/rev and v = 90 m/min .... 106 Figure 5:24 SEM micrograph of f = 0.2 mm/rev and v = 90 m/min .... 108 Figure 5:25 SEM micrograph of f = 0.1 mm/rev and v = 120 m/min 109 Figure 6:1 Schematic flow diagram of proposed methodology ............. 113 Figure 6:2 Finite element simulated cutting forces and temperature fields for feed rate 0.1 mm/ rev, Cutting speed (Vc) of 120 m/ min . 114 Figure 6:3 Comparison of experimental and simulated cutting forces .......................................................................................................................................... 114 Figure 6:4 Comparison of experimental and simulated cutting forces feed of 0.1 mm/ rev ................................................................................................ 115 Figure 6:5 Comparison of energy consumption between experimental and proposed method ............................................................................................ 116 Figure 6:6 Finite element simulated cutting temperature at Cutting speed (a) Speed of 150 m/ min and feed of 0.2 mm/ rev, (b) feed rate 0.3 mm/ rev, depth of cut 0.5 mm and cutting speed 104 m/ min ...... 119 Figure 6:7 Temperature distribution at the cutting tool (a) inlet velocity 0.1 m/sec, (b) inlet velocity 1 m/sec and (c) inlet velocity 10 m/sec ............................................................................................................................ 121 Figure 6:8 Temperature measurements along the rake face of the cutting tool velocity at 0.1, 1 and 10 m/sec ................................................... 122 Figure 6:9 Temperature distribution at the flank face of the tool at inlet velocity 0.1, 1 and 10 m/sec ............................................................................... 123 Figure 6:10 Concept used for the convective heat transfer using CFD model ............................................................................................................................. 124
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Figure 6:11 FE simulation coupled with CFD model for the optimum value selection of convective heat transfer coefficient ............................. 125 Figure 6:12 (a) FE machining simulation (b) Experimental cutting temperature (c) Simulated cutting temperature 1st Iteration with h = 100 W/m2K (d) FE Simulated Cutting temperature 2nd Iteration with h = 203.95 W/m2K ...................................................................................................... 127 Figure 6:13 (a) CFD simulation (b) Calculation of convective heat transfer coefficient close to tool tip ................................................................ 128 Figure 6:14 Experimental and simulated cutting temperatures from iterative process ........................................................................................................ 129 Figure 6:15 (a) Cutting temperature distribution on the tool (b) Temperature distribution along the line shown in (a) ...................... 130
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LIST OF TABLES
Table 2:1 : Brinell hardness number and machinability rating of common work materials [6] .................................................................................. 13 Table 2:2 Summary of MQL/ MQCL literature available for the machining of titanium alloy (Ti6Al4V) .............................................................. 26 Table 2:3 Material Constitutive models commonly used in machining simulations .................................................................................................................... 33 Table 2:4 Summary of the available FEA literature ................................... 36 Table 3:1 Nominal chemical composition of Ti6Al4V ................................. 50 Table 3:2 Mechanical properties of Ti-6Al-4V at room temperature ... 50 Table 3:3 Specificans of the turning cutting inserts [3] .............................. 51 Table 3:4 Experimental setups for Papers II - VII ......................................... 52 Table 3:5 Properties of vegetable oil used in mist [5] ............................... 56 Table 3:6 Specifications of Kistler dynamometer [6] .................................. 57 Table 3:7 Specifications of Mitutoyo tool maker microscope [8] ........... 58 Table 3:8 Specifications of Mitutoyo surface roughness tester [9] ....... 60 Table 3:9 Specifications of power logger .......................................................... 62 Table 4:1 Geometry of cutting inserts [1] ........................................................ 65 Table 4:2 Temperature dependent mechanical and thermo-physical properties for TiAlN coating .................................................................................. 66 Table 4:3 Johnson-Cook model parameters [2] ............................................. 67 Table 4:4 Properties of uncoated carbide for the CFD model [8] ........... 70 Table 6:1 Lifecycle estimates of gCO2e/ kWh for electricity generation procedures [1] ........................................................................................................... 117
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LIST OF ABBREVIATIONS
ANOVA Analysis of variance BUE BUL
Built up edge Built up layer
CCNG Compressed cold nitrogen gas CCNGOM Compressed cold nitrogen gas and oil mist CCS Carbon capture and storage CNC Computer numerical control CVD Chemical vapour deposition COF Coefficient of friction CFD Computational Fluid Dynamic FE Finite Element GDP Gross domestic product GHG Greenhouse gas emissions HPC High pressurized cooling HSS High speed steel LCA Life cycle assessment MRR Material removal rate MWF Metal working fluids MQL Minimum quantity lubrication PCD Polycrystalline diamond PCBN Polycrystalline cubic boron nitride PVD Physical vapour deposition SEM Scanning electron microscope TiC Titanium carbide WC Tungsten carbide
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LIST OF NOMENCLATURE
Symbol Parameter Units
FOR MACHINING CONCEPTS Rake angle degrees Friction angle degrees
Fc Cutting force N Ft Thrust forces N Fs Shear force parallel to shear plane N Fn Normal force perpendicular to shear
plane N
F Force at tool-chip interface N N Normal force at tool-chip interface N Ø Shear angle degrees i Inclination angle degrees VB Width of flank wear land mm Ra Surface roughness m Vc Cutting speed m/ min DOC Depth of cut mm f Feed mm/ rev fr Feed rate mm/ min
FOR FINITE ELEMENT AND COMPUTATIONAL FLUID DYNAMIC SIMULATIONS a, b, d Modified Johnson-Cook material model constant k Shear flow stress MPa m Johnson-cook material model constant
(strain rate sensitivity)
m° Frictional factor n Johnson-cook material model constant
(strain hardening index)
r Softening function parameter A, B, C Johnson-cook material model constant Cp Heat capacity J/K D Critical damage value E Modulus of elasticity MPa Fc Cutting force N
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K10 Uncoated carbide grade with high degree of temperature resistance
P Power consumed W S Softening function parameter SST Shear Stress Transport turbulence model T Workpiece temperature K Tm Melting point K T° Ambient temperature K Vc Cutting speed m/min
Thermal expansion K-1 True plastic strain Strain rate s-1 Reference strain rate s-1
Effective strain Thermal conductivity W/(m.K) Flow stress MPa
Maximum principal stress MPa Shear stress in friction MPa
u Velocity component in x direction m/ sec v Velocity component in y direction m/ sec w Velocity component in z direction m/ sec
Density kg/ m3 p Pressure Pa μ Viscosity Pa.s Cp Specific heat at constant pressure Jkg-1K-1 Ø Dissipation function
Conductivity Wm-1 K -1 w Expansion K-1
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SECTION IRESEARCH AIMS AND OBJECTIVES
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CHAPTER 1: INTRODUCTION
This chapter describes the background and aim of the research. Organization of the thesis is also elaborated in this chapter.
1.1 Background
Sustainability is a major driving force of today’s market. There is a need to inspire manufacturers to integrate sustainability into their corporate strategy to motivate both profits and growth. The concept of incorporating sustainability in manufacturing is termed as sustainable manufacturing. Sustainable manufacturing deals with the opportunities of reducing the utilization of resources without compromising the value creation/ addition associated with the finished product. To reduce the consumption of resources, local level innovations are being explored and implemented in the different phases of product’s life cycle from extraction to the delivery. Sustainable manufacturing emphasis on the concept of global value creation instead of the traditional productivity based measures. The ideology of global value creation comprises on economic gains, ecological benefits and social improvements. To evaluate the impact of local level efforts on a system level perspective, an integrated framework based on product development and supply chain design is required [1].
By implementing the sustainable practices in the metal cutting sector, environmental performance can be enhanced considerably under cost-effective conditions. The idea of sustainable manufacturing deals with effective use of material flow, energy, process knowledge, health safety, and environmental concerns. In the manufacturing sector, sustainable practices can be facilitated by minimizing the resources (energy, material, and water), improving environmental concerns by reducing
“The last thing that we find in making a book is to know what we must put first”
Blaise Pascal (French Mathematician)
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the use of toxic and non-biodegradable chemicals, efficiently designing life cycles, and improving working conditions (such as ergonomics and health safety) [2].
Generally cutting fluids are used in the machining processes due to their several functions like lubrication, cooling and ease in chip removal. On the other hand, these cutting fluids are toxic and non-biodegradable in nature. They can also cause problems to the human health by facilitating skin and breathing issues. The proper and safe disposal of these cutting fluids also requires special attention from consumers. In case of any mishandling related to disposal they pose critical danger to the soil and water resources. Therefore, strict environmental regulations have been imposed to limit the usage of cutting fluid in manufacturing sector. In Germany technical rules for hazardous substances (TRGS) have been published to limit the usage of hazardous substances [3]. Similar effort was performed by Spain to regulate the management of used oil under Decree 259/1998 29 of September [4]. A study conducted by Daimler Benz in Germany in the mid 1980’s identified the purchase, maintenance, and disposal of metalworking fluids as contributing 16% of their overall manufacturing costs [8]. In order to limit the usage of cutting fluids in machining, it is desirable to machine materials under dry cutting or near dry cutting environments. Near dry and minimum quantity lubrication (MQL) techniques are being explored to replace conventional flood cooling method to reduce environmental burden.
The sustainable machining concept aims to reduce the amount of greenhouse gas (GHG) emissions and the ecological footprint without compromising the surface integrity of the machined part. When it comes to the machining of difficult to machine titanium alloys the environmental burden is also higher. In order to reduce GHG emissions and the ecological footprint in machining, processing time and energy consumption also plays an important role. In order to conduct a desired machining operation on a certain machine tool, electrical energy is drawn from an electrical grid system. Electrical energy is generated by using different energy sources such as coal, fossil fuels, and hydraulic, nuclear, solar and wind energy. Each source produces different amounts of GHG emissions, but renewable energy resources
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generate significantly less emission. GHG emissions in machining can be reduced by utilizing electricity from renewable sources and by minimizing energy consumption during the machining phase.
The way to incorporate sustainability in metal cutting sector is by using proper design methodology. Such methodology can assist in lowering environmental burden of the engineering components using advanced design and analysis software packages.
1.2 Thesis Scope and Aim
Implementation of sustainable practices in the machining phase has always been a point of interest for manufacturers. In the core machining phase, consumption of cutting fluids and energy requirements are directly related to the environmental impact of the machining process involved to produce a certain feature. On the other hand, difficult-to-cut materials such as titanium and nickel based alloys are preferred over conventional steels and aluminium alloys due to their high strength to weight ratio, fracture toughness, fatigue strength, superior corrosion resistance and ability to operate at higher temperature. Particularly, in aerospace, resistance to crack initiation and growth, fatigue behaviour and resilience to creep makes the use of titanium alloys ideal [9]. The strong point of titanium alloys is that they show higher strength than Aluminium alloys and less density than steels, making them suitable for structural applications [5]. Due to the extraordinary properties, titanium and nickel based alloys are used extensively in the demanding sectors like aerospace, automotive, petrochemical, marine, military, biomedical and nuclear [6].
The literature review presented in chapter 2 shows that most of the research conducted on the sustainability aspects of titanium alloys’ machining was directed to explore the near dry or environmental friendly lubrication techniques and GHG emissions resulting from energy consumption. The literature shows that previously main focus was given to the cooling strategies such as conventional flood, emulsion based minimum quantity lubrication, high pressurized cooling and cryogenic machining. On the other hand, only empirical model have been developed to predict energy consumption involved in
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a machining process. The literature also showed the research gap that there are very few studies who have tried to incorporate computational fluid dynamic modelling in machining to study the interaction of cooling strategies.
This thesis envisages covering the gap in the research work on the utilization of minimum quantity cooling lubrication (MQCL) as a potential environmentally cooling strategy for the machining Ti6Al4V titanium alloy. Energy efficient machining can significantly reduce the associated environmental impact for the product. The thesis also aims to demonstrate potential use of finite element and computational fluid dynamic modelling techniques to facilitate the sustainable practise in the machining phase.
The scope of this thesis is limited to the experimental and numerical study of turning operation.
1.3 Research Problems The fundamental research questions addressed in this thesis concern are:
1. The feasibility of shifting from conventional (dry and flood) cooling techniques to the vegetable oil based minimum quantity cooling lubrication (MQCL), as an environmental friendly cooling technique.
2. The potential of using finite element (FEA) based modeling approach to predict the energy consumption involved in a machining process.
3. The potential of using computational fluid dynamic (CFD) modeling to simulate the cutting environment for reliable prediction of cutting temperature distribution on the cutting tool.
1.4 Scientific Contribution Summary of Appended Papers My scientific contributions enclosed in this thesis were initiated by the economical requirements to develop new methods of reducing the manufacturing impact in the machining of aerospace components and of the need to develop comparative evaluation methods from both
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machinability and environmental perspectives. The main contributions are in the areas of Ti6Al4V machinability study using MQCL (vegetable oil based) system, energy prediction in machining and temperature mapping on the tool using CFD modelling. The scientific contributions are summarized as mentioned below;
The state of the art study provided a systematic evaluation of cooling strategies from machinability and environmental perspective as demonstrated in Paper I. In Papers II and III, six different cooling strategies including flood cooling, dry machining, vegetable oil MQL machining, cooled air lubrication, cryogenic machining (with liquid Nitrogen), vegetable oil (MQCL) machining were evaluated for machining Ti6AL4V. To evaluate machinability, the studies considered tool life assessment, energy consumption, wear mechanisms, and machined surface quality. In Paper IV, vegetable oil based MQCL system was utilized in internal and external arrangements and machinability was studied in detail using cutting forces, tool wear, surface roughness and associated wear mechanisms. Paper V utilized vegetable oil based MQCL system to study machinability of Ti6Al4V using various oil flow rates. The use of rapeseed vegetable oil in MQCL configuration turns out to be an overall sustainable alternative. Thus, confirming the promise predicted by the use of vegetable oil as a lubricant for machining.
The research study introduced a new methodology based on FEM simulation for the reliable prediction of cutting energy as elaborated in Papers VI & VII. The FE based approach was implemented and verified experimentally. The numerical energy predictions were found in good agreement with experimental results.
The research study also proposed a novel CFD modelling of the environmental effect to predict the cutting temperature and temperature distribution on the cutting tool as elaborated in Paper VIII & IX. Paper X utilized the already developed CFD model and a coupling procedure was developed to get the optimum value of heat transfer coefficient.
7
Figure 1:1 Structure of the thesis
8
1.5 Thesis Structure The thesis consists of five sections and six chapters. This PhD thesis is structured in the form of compilation thesis, so that the chapters were kept brief while the scientific contributions are further presented in the appended papers. The thesis is outlined as follows; In Section I, chapter 1 provides a brief introduction about the research aims and objectives. Personal scientific contribution has also been discussed in this chapter. In Section II, chapter 2 provides a critical review of the current research on the machinability of titanium alloys and sustainability concepts in machining. In Section III, chapter 3 describes the experimental setup adopted to perform the aimed research. Chapter 4 explains the numerical methodology adopted to conduct the desired simulations required to perform the study. In Section IV, chapter 5 discusses the results observed from experimental study to evaluate the machinability of Ti6Al4V under MQCL (vegetable oil based) cooling method. Chapter 6 discusses the results obtained from finite element (FE) and computational fluid dynamic (CFD) numerical simulations. The section V provides conclusions from obtained results. Recommended future work is also provided. At the end, all original research papers are appended to provide complete details of experimental and numerical research work.
9
References [1] A. D. Jayal, F. Badurdeen, O. W. Dillon, and I. S. Jawahir,
“Sustainable manufacturing: Modeling and optimization challenges at the product, process and system levels,” CIRP J. Manuf. Sci. Technol., vol. 2, no. 3, pp. 144–152, Jan. 2010.
[2] A. K. Kulatunga, N. Karunatilake, N. Weerasinghe, and R. K. Ihalawatta, “Sustainable Manufacturing based Decision Support Model for Product Design and Development Process,” Procedia CIRP, vol. 26, pp. 87–92, 2015.
[3] R. Schlosser, F. Klocke, and D. Lung, Sustainability in Manufacturing – Energy Consumption of Cutting Processes. Advances in Sustainable Manufacturing, Springer Berlin Heidelberg, 2011, pp. 85–89.
[4] T. T. Rules, H. Substances, T. Committee, F. Ministry, S. Affairs, and J. M. Gazette, “Technical Rules for Hazardous Substances ( TRGS ) concerning restrictions on use , substitutes and substitution of processes or technology,” no. September, pp. 1–13, 2013.
[5] L. N. López de Lacalle, C. Angulo, a. Lamikiz, and J. a. Sánchez, “Experimental and numerical investigation of the effect of spray cutting fluids in high speed milling,” J. Mater. Process. Technol., vol. 172, no. 1, pp. 11–15, Feb. 2006.
[6] G. Weiping, X. Honglu, L. Jun, and Y. Zhufeng, “Effects of drilling process on fatigue life of open holes,” Tsinghua Science Technol., vol. 14, pp. 54–57, 2009.
[7] E. O. Ezugwu, Z. M. Wang, and A. R. Machado, “The machinability of nickel- J. Mater. Process. Technol., vol. 86, pp. 1–16, 1999.
[8] P. Johanssen, “Null Losung”, Mercedes Benz will Kuhlhlscherstoff reuzueren. Industrie Anzeiger, 1980.
[9] W. Shen, W.O. Soboyejo, and A.B.O. Soboyejo, "An investigation of fatigue and dwell fatigue crack growth in Ti–6Al–2Sn–4Zr–2Mo–0.1Si," Mech. Mater., 36, 117–140, 2004.
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SECTION IISETTING THE SCENE BY CONDUCTING
THE STATE OF THE ART IN SUSTAINABLE MACHINING OF TITANIUM ALLOYS
11
CHAPTER 2: STATE OF THE ART IN SUSTAINABLE MACHININGOF TITANIUM ALLOYS
This chapter presents a brief review of the available literature related to
the sustainable machining of Titanium alloys.
2.1 Environment Friendly Machining Concepts
Due to highly competitive global market, it is essential to conduct efficient machining processes with optimized machining cost, high surface integrity and keeping in view environmental sustainable considerations [1]. Due to the strict environmental rules and regulations metal cutting sector is under immense pressure for improving the environmental performance of machining processes. Sustainable manufacturing concepts offer a cost effective route to improve environmental and social performance [2]. Some benchmarked sustainable practices are mentioned below for improving the environmental performance of a machining process;
- Less waste generation by encouragement of remanufacturing or recycling
- Efficient utilization of resources (water, energy and materials) - Efficient management of metalworking fluids (MWF) - Betterment in working conditions - Adequate training of labour - Elimination of rework
There are two main aspects involved in the machining operation which directly contribute towards the environmental performance of the process. These two aspects are energy consumed by the machine and
“An investment in knowledge pays the best interest” Benjamin Franklin
(Scientist and Political Theorist)
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metalworking fluid application to dissipate heat during machining. Energy generation is one of the key contributors of carbon dioxide (CO2) emission in the environment. By reducing the energy utilized by a machining tool resulting CO2 emissions can also be reduced [3]. As regarding conventional metalworking fluids, they represent hazard for environmental due to their toxicity and non-biodegradable nature. To make machining process environment friendly in nature, toxicity of metalworking fluids has to be decreased whereas biodegradability has to be improved. To minimize the environmental issues associated with metalworking fluids usage sustainable approaches such as minimum quantity lubrication (MQL), minimum quantity cooling lubrication (MQCL), cryogenic, high pressure cooling (HPC) and nano-particles assisted cooling approaches should be investigated [4].
Titanium alloys are mainly utilized in the aerospace industry due to their durability, high fatigue resistance and ability to sustain high operating temperature. As titanium alloys are difficult to machine, due to which machining of these alloys ends up with higher environmental burden. The aerospace companies are now embracing the sustainable philosophy in order to reduce their carbon footprint. This means that the best sustainable practices have to be used in machining of titanium alloys as well in an effort to reduce the carbon footprint and greenhouse gas (GHG) emissions. But before to discuss the sustainable practices for the machining of titanium alloys, it is also important to understand that titanium machining is different from the machining of conventional metals.
2.2 Machinability of Titanium Alloys
Machinability is the ease with which material can be machined under specified set of cutting conditions [5]. Machinability is rather a relative concept, and depends on the end-user, that is assessed with reference to various key performance indicators such as the tool life, cutting force/ power consumption, surface quality, cutting temperature, chip formation, etc. Machinability is a complex function of many variables involved in the cutting process. Titanium alloy (Ti6Al4V) is termed as a difficult to cut material due to the following issues;
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High strength and hardness at elevated temperature, strength and hardness of the workpiece material has a controlling influence on the machining performance. The materials with high hardness value at elevated temperature are difficult to machine, as higher cutting forces and cutting temperatures are attained during the machining process. Higher hardness of the workpiece material can also speed up the tool wear and result in shorter tool life. Hardness values and machinability rating of several workpiece materials have been reported in Table 1.
Table 2:1 : Brinell hardness number and machinability rating of common work materials [6]
Workpiece Material Brinell Hardness
Number (BHN)
Machinability Rating
Base steel: B1112 180 -220 1.00 Low carbon steel: C1008, C1010, C1015
130-170 0.50
Medium carbon steel: C1020, C1025, C1030
140-210 0.65
High carbon steel: C1040, C1045, C1050
180-230 0.55
Free machining steels: 301, 302
170-190 0.50
Tool steel (unhardened) 200-250 0.30
Inconel 240-260 0.30 Inconel X 350-370 0.15 Waspalloy 250-280 0.12 Titanium (Ti6Al4V) 334 0.36
Low Thermal Conductivity, The inherent property of Ti6Al4V is the low thermal conductivity of the material. During the metal cutting process, energy is utilized to plastically deform and remove the workpiece material in the form of chips. As described in the available literature, major portion of this
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energy is converted into heat and causes high temperature formation in the cutting zone. The heat generated in the cutting zone is mainly taken away by the chip being removed. However a small amount of heat is dissipated through the workpiece and cutting tool. Due to the poor thermal conductivity (approximately 15 W/m °C) of titanium and its alloys heat cannot be easily rejected from the cutting zone. Chemical Reactivity, titanium and its alloys have very high chemical reactivity when temperature exceeds 500 °C [7]. Titanium and its alloys, at elevated temperatures, react with almost all of the cutting tool materials. Due to high chemical reactivity of titanium alloys, chips tend to weld at tool tip and cutting edge which results in catastrophic tool failure and severe edge chipping. High tendency of built up edge (BUE) formation is present due to high chemical reactivity of these alloys. Springback effect due to low Young’s Modulus, titanium has comparatively low elastic modulus as compared to other conventional materials. Low value of elastic modulus means higher strain with the application of less force. When cutting tool is engaged and cutting force is developed, titanium alloys as workpiece material springs back and provides a bounce during machining [8]. Due to the springback action poor dimensional accuracy is achieved. Self-Induced Chatter, Springback action is also one of the principle causes of chattering. Chattering also limits material removal rate (MRR) during machining operations. Aerospace industry is focusing to achieve higher material removal rates to increase production rate. Due to the chattering problem machine tool has to work under region specified by its dynamic stability. Bad surface finish, high level noise, excessive tool wear, low production rate, machine tool fracture, increase in scrap, increase in energy and high cost are some other drawbacks introduced by chattering [10]. Literature [11]
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reports that during machining phase cutting tool bounces back like a spring because of high elasticity of titanium. This bounce back action reduces clearance angle on flank face which facilitates chatter during the machining process. Cyclic nature of force produced due to the formation of segmented chips is another reason of excessive chatter. It was observed that titanium machining provides surface defects almost twice than carbon steel mainly due to the chatter. Higher chatter increases flank wear, vibration and cutting temperature [16-17]. Ezugwu et al. [14] has also elaborated the consequences of chattering when machining titanium alloys. Work Hardening Behaviour, The top most layers of the titanium alloys as workpiece material experience work hardening during the machining phase. When the cutting temperature reaches the value above 600 – 700 °C, atmospheric oxygen and nitrogen diffuses into the top most layer of workpiece and results in higher hardness level [15]. Another cause of work hardening is the high plastic deformation during the machining operation. Difficult Chip Formation, it has been reported that for titanium alloys segmented chips are formed at all levels of cutting speed. In segmented chip formation, material deforms plastically ahead the tool. Figure 2.1 shows schematic illustration of segmented chip formation. Fracture occurs in the form of shear band when certain strain level is reached in the cutting process. The chips formed under these conditions are segment like in shape [16]. The phenomenon of cyclic chip formation generates variable forces during machining phase [17]. It is reported in literature [21 -22] that cyclic nature of cutting forces are generated due to serrated chip formation and low Young modulus which results in excessive chatter on cutting tool.
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Figure 2:1: Schematic illustration of segmented chip formation [18]
2.3 Heat Partitioning when Machining Ti6Al4V
Heat generation is generally divided into three sources as shown in the Figure 2.2. Primary heat source mainly consists of the heat produced by the plastic deformation during the shearing action. Secondary heat source is present on the rake face of the cutting tool. It mainly consists of the friction present in between tool – chip interface. Tertiary heat zone consists of the heat generated due to the friction between flank face of the cutting tool and new surface being generated from machining.
Figure 2:2 Heat generation zones during the machining operation [20]
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Several studies from the previous literature [9-10] shows that approximately 50% of the heat generated can be transmitted to the cutting tool depending on the cutting tool material. The amount of heat transfer to the cutting tool can reach up to 80% when machining titanium and its alloys. A comparison of thermal load distribution between chip and tool for steel CK 45 and Ti6Al4V has been reported in Figure 2.3. This high amount of heat transfer in to the cutting tool accelerates different tool wear mechanisms and results in poor tool life.
Figure 2:3 Comparison of thermal loading for machining titanium and steel [9 -10]
When discussing the heat transferred to the tool during the machining process, it is also very important to point out that this heat transfer is more dominant in case of continuous cutting process than the intermittent cutting process.
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2.4 Environmental Issues in Metal Cutting Sector Utilization of Cutting Fluid; cutting fluids (MWF) were demonstrated by several research works to be essential in the machining of titanium alloys for increasing the tool life, improve surface finish and chip cleaning from the cutting zone. Metal working fluids acts as lubricant or/ and coolant during machining. These days, metal working fluids (MWF) are being questioned extensively for their economics and environmental related issues. These lubricants and coolants impose danger to environment and health due to their toxicity, non-biodegradability and higher energy consumption for use, maintenance and disposal. In order to make machining process sustainable in nature, toxicity has to be reduced whereas biodegradability has to be enhanced. This section provides a brief over view of different cooling strategies utilized to improve the machinability of titanium alloys.
Energy requirements in the Metal Cutting; machining operations are normally conducted on manual or computer numerical controlled (CNC) machine tools. These machine tools use electrical grid as an input energy. Greenhouse gas (GHG) emissions are directly linked with the energy sources a country utilized to generate electricity. If a country is using clean energy resources (solar, wind, geothermal and tidal etc) then the associated greenhouse gas (GHG) emissions will be lower than the electricity generated using conventional energy sources (fossil fuels, coal and hydal). The energy utilized by machine tool to perform certain machining task is directly proportional to the production of greenhouse gas (GHG) emissions. By optimizing the electricity consumption in machine tool greenhouse gas (GHG) emissions can also be reduced. For example Li et al. [22] provided a review of energy consumption by considering six different machine tools covering different manufacturing process as shown in Figure 2.4. Energy consumption of a milling machine has also been shown in Figure 2.5.
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(a)
(b) (c)
Figure 2:4 (a) Machine tools selected (b) Description of power (kW) consumption (c) Average percentage energy breakdown of the reviewed machine tools [22]
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Figure 2:5 Energy consumption breakdown for milling process [97]
2.4.1 Proposed Sustainable Solutions for the Metal Cutting Sector
Energy Requirements in Metal Cutting
Several researchers have focused their work to establish a link between machining process and the environmental issues associated with machining operations. Munoz and Sheng [23] provided an analytical model to demonstrate the environmental issues during machining phase. The study was based on the energy consumption, tool wear, cutting mechanics and lubrication. The study concluded that energy consumption is strongly linked with geometric features, material type and lubrication technique. Kordonowy [24] conducted a very detailed study on energy consumption by machine tools. The work considered six different machines (injection molding, manual and
21
automatic milling and automatic lathes) during the study. The study identified the portions of energy, consumed during different operations of the machine tool. Figure 2.5 shows the breakdown of energy consumption during machine operation on a milling machine.
Figure 2:6Characterization of power consumption during cutting processes [25]
Dahmus and Gutowski [26] performed a study to investigate the environmental aspects of machining. The study revealed that energy consumed in cutting process is comparatively lower than the total energy consumed during machining cycle. Drake et al. [27] developed a framework to characterize energy utilization of the machine tool. The framework was composed of six steps based procedure. The framework suggested that major portion of energy consumption was utilized by the machine tool controllers. The spindle system utilized 35% of the total energy consumption. Schlosser et al. [25] also developed a model for energy consumption during the manufacturing operation. Figure 2.6 shows the characterization of power consumption during machining operation. The model was verified for drilling process. The model was found in good agreement with the experimental results.
Mativenga and Rajemi [28] reviewed formerly proposed methodology to select optimum cutting parameters. The study provided a link
22
between energy intensity and energy related cost involvement. Mori et al. [29] studies the behaviour of power consumption in machining centers using different sets of energy cutting parameters. The study provided useful data about the behaviour of energy consumed by spindle motor and servo motors. The study suggested that to optimize energy consumption of the process, synchronisation of spindle acceleration and feed system is critical. Neugebauer et al. [30] executed machining experiments based on turning and drilling operations. The study was designed to analyse energy efficiency in machine tools. The study incorporated tool selection and cutting parameters as well. The study exposed that tool selection and feed rate play significant role towards energy efficiency of machine tool. Balogun and Mativenga [31] also established a mathematical model to predict direct energy consumption of the machine tool. The new model covered the deficiencies of previously created models. The studied efficiency created a link between the energy consumption in machine modules, spindles, auxiliary units and motion states during machining. However, the power involved in ventilation and lighting cannot be neglected.
Minimum quantity lubrication /cooling lubrication (MQL/MQCL) strategy
Several researchers have investigated the machining performance under different environmental friendly cooling strategies in the past decade. However, the main emphasis was given to the near dry machining or MQL based techniques. Near dry machining or minimum quantity lubrication techniques are used in some cutting processes to encourage sustainable practices in the machining sector. In these methods, high speed air jet is introduced with micro-drops of vegetable oil in suspension to lubricate the cutting zone. In these techniques, lubricate flow rate is limited to millilitres/ hour instead of litre/ min like in flood cooling environment [32]. In order to keep the relation between dry cutting on one hand and on the other hand flood cooling, minimum quantity cooling lubrication (MQCL) is utilized. In MQCL system, compressed low temperature air is used to act as coolant and limited biodegradable oil is used in the form of mist to overcome the friction at the cutting zone [33].
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Pejryd et al. [34] investigated the machining performance of aero-engine alloys under minimum quantity cooled lubrication in turning operation. The study was focused on Inconel 718, Waspaloy, stainless steel and titanium alloy 6Al4V) using uncoated carbide and whisker reinforced ceramic based cutting tools. Ester and fatty alcohol based cutting fluids were utilized in MQL arrangement and air with 4°C was mixed to develop MQCL setting. The study did not observe any remarkable difference in the cutting forces due the application of MQCL. However, tool life was increased when machining titanium alloy using MQCL setting. Ramana et al. [35] performed turning experiments on Ti6Al4V using MQL arrangements. The study focused on the optimization of process parameters using Taguchi’s Robust Design Methodology on the tool wear.
Liu et al. [36] conducted an experimental study using MQL based arrangements on Ti6Al4V. The study provided methodology to select optimum cutting parameter using coupling response surface methodology (CRSM). To develop the model, Taguchi design of experiments and multiple regression model was used. ANOVA was utilized to found that feed rate was the dominant factor towards surface roughness and cutting forces. Lowest level of feedrate and depth of cut provided lowest surface roughness and cutting forces. Liu et al. [37] performed turning test on Ti6Al4V using uncoated carbide and two new nano-composite coatings, (nc-AlTiN)/ (a-Si3N4) and (nc-AlCrN)/(a-Si3N4). The study pointed out at the importance of proper matching of coating material with MQL lubricant as the study found (nc-AlTiN)/(a-Si3N4) coated tool more suitable than (ncAlCrN)/(a-Si3N4) coated tool when cutting titanium alloy. Wang et al. [38] explored the usage of MQL arrangement in continuous and interrupted turning processes using Ti6Al4V. The MQL results were compared with dry and flood cutting environments as well. By analysing the cutting forces obtained during each cooling strategy mean friction coefficient at the tool-chip interface has been evaluated. The study investigated the cutting forces experimentally and by finite element machining simulations. The study revealed that MQL outperformed flood cooling at higher cutting speeds because of improved lubrication capability.
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The study also showed that MQL was more effective in the case of interrupted cutting.
Okada et al. [39] utilized a solid and indexable drills with (Ti,Al)N+ TiN-coated carbide with two oil holes to perform drilling operation using MQL system. MQL system used vegetable oil and water insoluble to form mist with supply pressure of 0.5 MPa and flow rate of 6 and 44 ml/h. The study showed that indexable drill combined with MQL technique provided low cutting temperatures and tool wear for the machining of Inconel 718 and Ti6Al4V. Rahim and Sasahara [40] investigated the potential of palm oil in MQL system for the drilling of Ti6Al4V. The study compared the results of palm oil with synthetic ester based MQL, cooled air and flood cooling environments. The study revealed that palm oil based MQL system out performed synthetic ester based MQL and flood environment. Dry air provided poorer results in term of cutting forces, tool life and energy consumption. Zeilmann and Weingaertner [41] used MQL based system to perform drilling experiments on Ti6Al4V using uncoated and coated drills (TiALN, CrCN and TiCN). Cutting temperature was evaluated for each cooling strategy. The study revealed that internal MQL performed comparatively better than external MQL system.
Vazquez et al. [42] performed micro-milling experiments on Ti6Al4V under MQL arrangements. Tungsten carbide cutter with two flutes was utilized to perform the cutting process under dry, MQL in feed direction, MQL against feed direction and with jet application. The study measured tool wear, surface roughness, accuracy, geometric shape and burr formation for all cooling strategies. The study revealed error of 20% in case of flood jet cooling that can be linked with the non-uniform heat flux on the cutting tool. Best results for tool life and burr formation were observed in case MQL. Park et al. [43] performed an experimental study to compare the eco-friendly lubrication options for the face milling of Ti6Al4V. The study considered cryogenic, laser assisted machining (LAM) and MQL techniques for the present work. All three methods provided encouraging results when compared with conventional flood and dry cutting modes. MQL showed good potential with the advantage of having simple hardware and setup.
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Liu et al. [44] performed experimental study on Ti6Al4V using MQL system in external arrangement. The study investigated MQL operating parameters such as air pressure, spraying distance, spraying angle and quantity of oil for the optimum selection. It was found that penetration into the cutting zone can be increased by air pressure and spraying distance. The optimum values of spraying distance and air pressure were found to be 25 mm and 0.6 MPa respectively. Qin et al. [45] conducted helical milling experiments to study the machining performance of an MQL system using Ti6Al4V. The study evaluated cutting forces, tool wear and wear mechanisms using CrN coated solid carbide end milling cutters. The MQL performance was found comparable with flood cooling in term of surface roughness and cutting forces. However tool life was found better than flood cooling.
Cia et al. [46] conducted end milling machining experiments to explore the effects of operating parameters like oil flow supply, compressed air supply and nozzle orientation etc. The experimentation was performed using flow rates of 2 ml/h – 14 ml/h. The study exposed that higher diffusion wear was observed for low oil supply rates ranging between 2ml/h – 10ml/h, however at 14ml/h no diffusion wear was observed. Yasir et al. [47] performed milling experiments using physical vapour deposition (PVD) coated cemented carbide tools using Ti6Al4V under MQL system. The study investigated the machining performance for cutting speed ranging between 120 - 150 m/ min and flow rates of 50 – 100 ml/h. It was observed that mist performed the best at cutting speed of 135 m/min. At 135 m/min and higher flow supply enhanced tool life was experienced. It was also observed that MQL system performed better under worn tool conditions.
Su et al. [48] investigated the machining performance of dry, conventional flood, nitrogen-oil mist, compressed cold nitrogen gas
C, and compressed cold nitrogen gas and oil mist (CCNGOM) cooling strategies. In order to evaluate the machining performance of each cooling strategy tool life and tool wear was considered. The study pointed out that compressed cold nitrogen gas and oil mist (CCNGOM) cooling strategy performed better than others and provided enhanced tool life. Sun et al. [49] conducted milling experiments on Ti6Al4V using MQL system. The study investigated tool
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life, wear mechanisms and cutting forces in air, flood and MQL environments. The study revealed that MQL produced low cutting forces at higher cutting speeds. MQL showed better potential at higher material removal rate due to better lubricity as compared to the flood cooling.
Table 2:2 Summary of MQL/ MQCL literature available for the machining of titanium alloy (Ti6Al4V)
No. Strategy/ Investigator
Cutting Parameter
s
Workpiece/ Tool
Cooling Strategy Conclusion
Turning Process
1
MQCL Pejryd et al., (2011) [34]
Carbide tool (Vc= 40 -80 m/min), Ceramic tool (Vc= 150 – 250 m/ min) Depth of cut = 1.25 mm,
- Inconel 718 - Waspaloy - 13Cr steel - Ti6Al4V / Tools (H13A and CC 670)
Two types of MQCL system 1. Ester (Acculube LB2000) 2. Fatty alcohol (Shell Garia SL501) with air at 4 C°
- No remarkable difference in the cutting forces - Extended tool life when machining Ti6Al4V using MQCL
2
MQL Ramana et al., (2014) [35]
Vc=63,79, 99 m/min Fr=0.206,0.274,0.343 mm/rev DoC=0.6,1,1.6 mm
Ti6Al4V / 03 different types of carbide tools 1.Uncoated grade 883 2. CVD coated TM 4000 3.PVD coated TS 2000
MQL setup consists of air compressor, spray gun with fine nozzle and cutting fluid chamber. supply rate was 100 ml/hr, mixed with compressed air (3bar)
- MQL machining shows favourable and better results compared to dry and flooded conditions - The analysis of variance (ANOVA) found that cutting speed is the most dominant variable for the tool wear.
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3
MQL Liu et al., (2013) [36]
Vc=40,80,120,160 m/min F=0.05,0.1,0.15,0.2 mm/rev DoC=0.3,0.6, 0.9,1.2 mm
Ti6Al4V / (nc-AlCrN)/ (a-Si3N4) nano coated tungsten carbide tool
MQL system was external spraying. MQL oil was from vegetable oil, Oil delivery [16 ml/h], Air Pressure[0.7Mpa], Spray distance 20mm on flank face
ANOVA found that feed rate was the main factor towards surface roughness and cutting forces. Lowest feedrate and depth of cut provided lowest surface roughness/cutting forces.
4
MQL Liu et al., (2013) [37]
Vc=120m/min F=0.1 mm/rev DoC=1.2 mm
Ti6Al4V / - Uncoated insert - Coated carbide with nano composite PVD coatings [(nc-AlTiN) /(a-Si3N4) and (nc-AlCrN)/ (a-Si3N4
External spraying MQL system BLUEBE FK with MQL vegetable oil Lb-1, Air flow rate 125l/min, air pressure 0.7MPa,Spraying distance 20 mm, Oil consumed 16 ml/h, Spraying at Flank face
The study pointed out on the importance of proper matching of coasting material with MQL lubricant as the study found (nc-AlTiN)/(a-Si3N4) coated tool more suitable than than (ncAlCrN)/(a-Si3N4) coated tool when cutting titanium alloy
5
MQL Wang et al. (2009) [38]
Vc=60,75,90,105,120 m/min, Fr=0.10,0.15mm/rev
Ti6Al4V / -CBN - H1 (5% Co) -Ceramic (Si3N4)
MQL system, Ecocool SCO-5 system with Ebara atomizer for vegetable oil mist
- MQL out performed flood cooling at higher cutting speeds because of improved lubrication capability
Milling Process
1. `
MQL Vazquez et al., (2015)
Spindle speed = 30,000 per min, Feed rate = 75mm/min Radial depth
Ti6Al4V / Mitsubishi tungsten carbide cutting tools. Flat end mills
In MQL system vegetable oil TRI-Cool MD1 by TRICO with viscosity of 34 cSt, In Flood cooling
Error of 20% in case of flood jet cooling that can be linked with the non-uniform heat flux on the cutting tool.
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[42] of cut = 200mm Depth of cut per pass=20 mm, Feed per tooth=1.25 mm/tooth
with a 200 mm dia and 02 flutes
vegetable oil MAK KIT10 ES-AL by LUBRICORP with viscosity of 1.3 cSt
Best results for tool life and burr formation were observed in case MQL
2.
MQL, LAM Cryogenic Park et al., (2014) [43]
Vc=50,80 m/min Feed 0.15 mm/tooth Axial depth 2 mm Radial depth 16.7 mm
Ti6Al4V / CoroMill 245 (Sandvik Corp.) with coated (PVD TiAlN) and uncoated carbide inserts
MQL spraying system (Uni-Max 202F by UNIST Inc.) Fluid was Coolube 2210, UNIST Inc. Air flow rate of 3 ml/min
- MQL showed encouraging results when compared with flood cooling in terms of energy consumption, tool wear and cutting forces.
3.
MQL Liu et al., (2011) [44]
Vc= 150m/min Feed = 0.05 (mm/tooth) Axial depth of cut (mm) 5 Radial depth of cut (mm) 1
Ti6Al4V / Kennametal inserts with coating (PVD TiAlN)
External MQL system BLUEBE FK with MQL vegetable oil Lb-1 was used Air flow rate (125 l/min) Air pressures 0.1, 0.3, 0.5, 0.7 MPa Spraying distances of MQL nozzle 15, 25, 35, 45 mm Oil consumed 2, 4, 6, 8, 10 ml/h Spraying angles 45, 90, 135
It was found that penetration into the cutting zone can be increased by air pressure and spraying distance. Optimum value of spraying distance was found to be 25 mm Optimum value of air pressure was found to be 0.6 MPa
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4.
MQL Qin et al., (2012) [45]
Speedle speed =2500, 3000, 4000,5000rpm, Axial feed per helical rev=0.10, 0.15, 0.20 and 0.25 mm, Tangential feed per tooth=0.03, 0.04, 0.05 and 0.06 mm/tooth
Ti6Al4V / CrN coated solid carbide end mills 04 teeth, overall length of 70 mm, Cutting edge length of 14 mm, Dia of 6 mm, helix angle= 30º rake angle = 12 º
MQL Palm oil (external, two nozzles) Air pressure0.5 MPa Lubricant flow rate = 10 ml/h Outlet air flow= 10000 l/h
MQL performance was found comparable with flood cooling in term of surface roughness and cutting forces. However tool life was found better than flood cooling.
5.
MQL Cia et al. (2012)[46]
Vc= 200 m/min Feed (fz) 0.25 mm/tooth Axial depth of cut (ap) 5 mm Radial depth of cut (ae) 1 mm
Ti6Al4V / Kennametal ED.T1805 type indexable cutting tool with two flutes coated with (TiAlN by PVD
MQL system was Blube FK with one external nozzle Lubrication oil Blube LB-1 Oil supply rate 2, 6,10 and 14 ml/h Air pressure 0.6 MPa Air flow rate 125 l/min Nozzle position 135° Nozzle distance 25 mm
Higher diffusion wear was observed for low oil supply rates ranging between 2ml/h – 10ml/h, however at 14ml/h no diffusion wear was observed.
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6.
MQL Yasir et al. (2009) [47]
Vc = 120,135,150 m/min, Fr = 0.1-0.125 mm/tooth, Axial DoC=2-2.5 mm, Flow rate =0,50,100 ml/h
Ti6Al4V / PVD coated cemented carbide
MQL System used Cutting fluid with, Specific gravity= 0.874 g/cm3 Viscosity = 22.2 mm2/s Flash point= 178C
It was observed that mist performed the best at cutting speed of 135 m/min
7.
MQL mixed with Nitrogen Su et al. (2006) [48]
Vc = 400 m/min F = 0.1 mm/rev Axial depth of cut = 5.0 mm Radial depth of cut = 1.0 mm
Ti6Al4V / TiN/TiC/TiN coated carbide
Nitrogen pressure, 0.6 MPa, nitrogen flow rate, 120 l/min; cutting oil, UNILUB 2032; oil discharging amount, 120 ml/h; - Dry, - Flood coolant, - Nitrogen-oil-mist, Compressed cold nitrogen gas(10 C) - Compressed cold nitrogen gas and oil
C)
- Cold nitrogen gas and oil mist (CCNGOM) cooling strategy performed better than others and provided enhanced tool life.
8.
MQL Sun et al., (2006) [49]
V= 40–140 m=min, F= 0.05–0.2 mm=rev, Axial depth of Cut=0.5 mm, Radial depth of cut=2–8 mm, Tooth = 02.
Ti6Al4V / Sumitomo CHE 2000 type of material H1 (carbide tool) and edge shape A
MQL system with Ebara Atomizer, air pressure 0.52 MPa, flow rate of 2–10 ml/h, Dia particle = 3.7 μm, Flood cooling using EcoCool S-CO5.
MQL produced low cutting forces at higher cutting speeds MQL showed better potential at higher material removal rate.
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Drilling Process
1.
MQL Okada et al., (2014) [39]
Vc=25, 50 m/min Fr=0.05, 0.10 mm/rev Hole Diameter =5.0 and 11.0 mm
AISI 1045, AISI 304, Ti6Al4V and Inconel 718 / Solid and Indexable drills with (Ti,Al)N+ TiN-coated with two oil holes
Lubrication MQL (vegetable oil and water insoluble) Supply pressure p=0.5 MPa Flow rate q=6 and 44 ml/h
Indexable drill provided low cutting temperatures for the machining of Inconel 718 and Ti6Al4V
2
MQL Rahim and Sasahara (2011) [40]
Blind holes were drilled using, Vc = 60m/min Depth of hole=10mm Feed rate = 0.1 mm/rev
Ti6Al4V / Mitsubishi (AlTiN coated carbide drill) Point angle = 130°, Helix angle = 30°
MQL (External) system used ; - Synthetic ester oil (MQLSE) - Palm oil (MQLPO)
The palm oil based MQL system out performed synthetic ester based MQL and flood environment
3
MQL
Zeilmann and Weingaertner (2006) [41]
vc = 10–50 m/min, Feed = 0.1–0.2 mm
Ti6Al4V / K10 carbide drills with and without hard coating (TiAlN, CrCN or TiCN)
MQL ( Externally and Internally) and Emulsion based flood cooling
Internal MQL out performed flood cooling and external MQL system
The extensive literature review of MQL/ MQCL strategy shows that many researchers have explored the machinability of Ti6Al4V using dry, conventional flood, mineral oil MQL, vegetable oil MQL, chilled air and liquid nitrogen as cooling strategies. The literature shows that although MQL is considered as strong option to replacing the conventional flood cooling, though MQL is mainly developed to facilitate the lubrication process instead of cooling process. Elevated heat generation during the machining of titanium alloy limits the performance of MQL technique due to missing component of cooling in it. There is a need to provide the missing component of cooling in MQL technique using hybrid cooling concepts.
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2.5 Advances in FEA Modeling for Machining Ti6Al4V The aims of conducting the critical review of FEM machining simulation were mentioned as: (1) to evaluate cutting forces for various cutting conditions and by this to predict energy consumption during machining operations, and (2) to estimate the temperature in cutting zone and used to predict the effect of various cooling methods. In order to predict reliable simulated cutting force, stress and cutting temperature, FEA model should incorporate proper material constitutive model, friction law and the damage model.
Material Constitutive Models
To simulate the machining operation with reliable accuracy it is advised to model the flow stress behaviour of workpiece material as accurate as possible. Constitutive material equations have been developed by researchers to accurately describe the flow stress behaviour of the workpiece material. Generally the equation of material constitutive law shows the flow stress behaviour as a function of strain, strain rate and temperature. The most commonly used material constitutive models are known as Usui, Oxley, Johnson – Cook and Zerilli – Armstrong. These constitutive models are shown in Table 2.3.
Many researchers of metal cutting subject such as Umbrello et al. [50], Özel and Zeren [51] and Rui et al. [52], prefer to use Johnson – Cook constitutive equation when modeling Titanium alloy (Ti6Al4V) as workpiece material. The Johnson - Cook constitutive model is capable to accommodate high strain rates and temperatures with large strains. Arrazola and Özel [53] pointed out that accuracy of the predicted results highly depends upon the modeling method, constitutive model for material flow stress, boundary conditions (heat transfer) and frictional law at tool-chip interface. In another study Özel et al. [54] investigated the machining performance of Ti6Al4V using uncoated and coated tools. The study developed a modified constitute model to simulate an accurate chip formation. Finite element based simulations
33
predicted cutting forces and tool wear. Simulated results were found in good agreement with experiments.
Table 2:3 Material Constitutive models commonly used in machining simulations [68, 69]
Name Constitutive equation
In another study Özel and Sima [55] incorporated different versions of Johnson-Cook constitutive model in their finite element simulations to predict the cutting performance of Ti6Al4V. The modified material models coupled the effects of flow softening, strain hardening and thermal softening effects. The study revealed that flow stress behaviour of material greatly influences the temperature generation and cutting forces. Umbrello [56] has also conducted a finite element simulation based study on high speed machining of Ti6Al4V alloy. The study was focused on predicting cutting forces and chip segmentation and morphology. The experimental results found in good compromise with the simulated results. Split Hopkinson’s pressure bar method (SHPB) is used to find the values of Johnson-Cook parameters. Literature [57] recommends that Split Hopkinson’s pressure bar method (SHPB) should be referred as a starting point for the identification of Johnson-Cook parameters. For accurate machining prediction, Split Hopkinson’s pressure bar method (SHPB) should be used in combination with machining tests and analytical chip segmentation models.
34
Friction Laws
Friction modelling plays an important part on the simulated results such as cutting forces, temperature and tool wear in the machining simulation. To simulate the metal cutting operation, it is necessary to define friction model at the tool chip interface. Several researchers have focused their work to develop and incorporate the friction behaviour at the tool – chip interface to get reliable results from simulation. These friction laws are presented in the upcoming section.
Shear friction law, It says that frictional stress on the rake face is proportional to the shear flow strength of the work material. Shear friction law is used to define the severe contact conditions in metal cutting [58].
2.1
Where m is frictional factor, is frictional shear stress and k is work material’s shear flow stress.
Coulombs friction law, in the early era of metal cutting research, researchers were using coulombs law of friction to model the friction behaviour at tool – chip interface. As per coulombs friction law, frictional stress is proportional to the normal stress. The coefficient of friction is used as a constant of proportionality. Coulomb friction law is represented below.
2.2
Where μ stress.
Hybrid friction modeling, the concept was presented by Zorev [59] that friction on rake face is divided into two regions named as sticking and sliding regions. The study revealed that normal stresses are very high in the sticking region and start decreasing in the sliding region. Therefore, sticking regions should be modelled under shear friction
35
law and sliding region can be modelled using Coulomb’s friction law as shown in the Figure 2.7.
Figure 2:7 Frictional shear and normal stress distribution over the rake face of tool [53]
Damage Model and Chip Separation
In the numerical modeling of a machining scenario, fracture is induced either by deleting elements or by element separation when certain level of deformation and stresses is achieved. When surface finish and integrity is being predicted by the numerical model then incorporation of accurate damage criteria is very important and critical. The most commonly used fracture criteria used in the machining simulations is known as Cockroft and Latham damage criterion [60].
The fracture criterion generally controls the segmented chip formation when machining titanium alloys [61]. As per this damage model, fracture starts when integral of highest principal stress component over a strain path becomes equal to the certain critical damage value.
36
Table 2:4 Summary of the available FEA literature
Previous Literature
FE Model Used Cutting
Parameter Experiment Verification
Paramet--ers Studied Tool 2D/ 3D
Friction Model Plastic Model
Ref [58] Rui et al.
3D Columb
strain hardening, thermal softening,
rate sensitivity
Vc = 55 & 90 m/ min
Cutting forces verified
experimentally
Residual stresses, cutting
forces & temperature
WC/ Co &
TiAlN coated
Ref [59] Arrazola, P.J. and Özel, T.,
2D Hybrid models
(Shear + Columb) Basic JC model
Vc = 300 m/ min, f = 0.2 mm/rev &
DoC = 1 mm
Forces were verified
experimentally
Forces, Temperature
& contact shear
stresses
Carbide tool
Ref [60] Özel, T. et al.,
2D Hybrid models
(Shear + Columb) Modified JC Model
Vc = 120 m/min & f = 0.075, 0.1 , 0.125 mm/
rev
Forces & tool wear
Forces, Temperature & wear rate
WC/ Co,
cBN & TiAlN coated
Ref [61] Sima, M. and Özel, T.,
2D Hybrid models
(Shear + Columb) Modified JC Model
Vc= 121.9 &240.8 m/
min
Forces measured
experimentally
Forces, Temperature
Effective Strain & Chip morphology
WC/ Co &
TiAlN coated
Ref [62] Umbrello, D.,
2D Shear Model
Basic JC model
Vc = 120 m/min, f =
0.127 mm/rev & DoC =2.54
mm
Forces Chips
Measured experimentall
y
Forces & Chip
morphology
Uncoated WC –P20
Eq. 2.3 represents the Cockroft and Latham damage model. The critical damage value was used as 0.6, which is a general value for machining simulations.
= 2.3 Where f is effective strain, 1 is maximum principal stress and D is critical damage value based on material.
37
The critical review of FEM simulations revealed that it is very important to use the proper material model to capture the effects of flow softening, strain hardening and thermal softening. Because the flow stress behaviour of material greatly influences the temperature generation and cutting forces. At the same time friction modelling plays an important part on the cutting forces, temperature and tool wear in the machining simulation.
2.6 Utilization of Computational Fluid Dynamics (CFD) Modeling in Machining Computational fluid dynamics approach is mainly based on the basic principles of fluid dynamics. Fluid dynamics is governed by the continuity, momentum and energy equations. Computational fluid dynamic provides a very cost effective technique of simulating the fluid flow by solving the governing equations numerically [62].
Governing equations of computational fluid dynamics are based on the conservation laws of mass, momentum and energy [62]. Conservation of mass equation means that the mass of the fluid is conserved and it is represented below in Eq (2.4), which is also known as the continuity equation [63].
+ ( ) + ( )+ ( ) = 0 2.4
The conservation of momentum equation is based on the Newton’s second law of motion (F = ma). It states that the rate of change of momentum equals to the applied forces. It is also known as Navier – Stokes equation. [63].
Conservation of energy is governed by the first law of thermodynamics. When applied to the fluid element in movement the first law of thermodynamics states that the rate of change of energy inside the fluid element is equal to the sum of the net heat flux into the element and rate of work done on fluid element by the body and surface forces. The energy equation [63] is mentioned below in Eq. (2.5);
38
+ + + = + + + + + + 2.5
The computational domain of the conjugate CFD model consists of the fluid and solid domains. Generally CFD software packages are used to develop the geometry, mesh and solution of the problem. At solid – fluid interface, similar conditions of heat flux and temperature are solved using both fluid energy and solid energy equations. The temperature distribution on the cutting tool can be studied using CHT methodology where fluid domain can be considered as the cutting environment and solid domain can be the cutting tool. Due to the broad application of CHT analysis researchers have used the methodology in kinetic heating, turbo-machinery, vehicle aero-dynamics, laser irradiation applications and duct heating as well [71-72].
Due to the very different nature of CFD modeling it was found very occasional when researchers have used CFD model to predict heat transfer in the metal cutting. However, there are very few studies available in literature where CFD modeling was conducted to predict behaviour in machining operation. Obikawa et al. [66] utilized computational fluid dynamics (CFD) modeling to analyse oil mist flow of MQL arrangement when machining (micro-turning) Inconel 718. Finite element volume method was employed to model a spraying nozzle and workpiece material. After the modeling, velocities and mass flow rates of three different nozzles were estimated using a micro-liter lubrication system. The study revealed the relationship of tool life with changing mist oil flow rate. The study complemented at the optimum spraying condition by providing flight distance of oil droplet.
In another study, Vazquez et al.[67] utilized computational fluid dynamics (CFD) analysis to investigate the behaviour of MQL for miro-milling when machining Ti6Al4V. The MQL system was implemented by using 200 mm diameter micro-end mill and result were compared with dry and jet applications. The machinability of MQL system was investigated using tool life, surface roughness and burr formation. CFD
39
modeling was incorporated in order to study influence of cooling fluid in the cutting zone in micro-milling process.
2.7 Research Gaps from Literature Review The research gaps observed by conducting the literature review exercise revealed the potential areas for a sustainable metal cutting. Research can be performed further in these areas to enhance the knowledge and understanding of metal cutting community, especially in titanium alloys machining. These potential areas have been summarized below;
There is a need to study and develop a green cooling strategy that has potential to replace the old conventional flood cooling method to minimize the environmental and health impact of metal working fluids in the metal cutting sector. The potential of hybrid concept (two or more strategies combined) based green cooling strategies should also be explored. There is a need to explore the potential of numerical modeling approaches to virtually asses the amount of energy consumed during the cutting phase. If the machine tool’s energy behaviour is known, idle energy utilized by the machine tool can also be estimated. The methodology can be explored and upon validation can be used to develop energy based sustainability calculator. The approach provides flexibility to the user to estimate the carbon foot prints involved during machining without physically performing the machining operation on a machine tool. It is rarely found in literature to find the application of conjugate heat transfer based computational fluid dynamic analysis of the cutting tool problem. The CFD approach of studying the thermal loading of the solid cutting tool should be investigated to study the interaction of cutting environment on the cutting tool with thermal load. The CFD methodology has potential to develop mist particles and MQL interaction on the cutting tool can also be investigated.
40
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[65] Y. Peigang, C. Ying, S. Liang, and Z. Jiaqi, “Application of conjugate heat transfer and fluid network analysis to improvement design of turbine blade with integrated cooling structures,” Proc. Inst. Mech. Eng. Part G J. Aerosp. Eng., vol. 228, no. 12, pp. 2286–2299, Dec. 2013.
[66] T. Obikawa, Y. Asano, and Y. Kamata, “Computer fluid dynamics analysis for efficient spraying of oil mist in finish-turning of Inconel 718,” Int. J. Mach. Tools Manuf., vol. 49, no. 12–13, pp. 971–978, Oct. 2009.
[67] E. Vazquez, D. T. Kemmoku, P. Y. Noritomi, J. V. L. da Silva, and J. Ciurana, “Computer Fluid Dynamics Analysis for Efficient Cooling and Lubrication Conditions in Micromilling of Ti6Al4V
46
Alloy,” Mater. Manuf. Process., vol. 29, no. 11–12, pp. 1494–1501, Oct. 2014.
[68] P.L.B. Oxley, "The mechanics of machining: an analytical approach to assessing machinability," Ellis Horwood, Chichester, 1989.
[69] A.P. Markopoulos, "Finite element method in machining processes," Springer London Heidelberg New York Dordrecht, 2013. (DOI 10.1007/978-1-4471-4330-7)
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SECTION IIIRESEARCH METHODOLOGY
48
CHAPTER 3: EXPERIMENTAL METHODOLOGY
experimental work. The experimental work was performed to investigate the machining performance of Ti6Al4V
strategy.
This chapter provides information about the experimental methods and techniques utilized during this research work. Detailed description is provided in Papers II – X. The chapter also provides detailed information about the workpiece and tool materials used, design of experiments, actual test setups and equipment utilized to measure input and output machining variables.
3.1 Design of Experiments (DOE) Any metal cutting operation is governed by different variables. These process variables have a controlling influence on the operation. Therefore it is essential to explore the opportunity of controlling these variables to make the operation as efficient as possible. Design of experiments (DOE) is a systematic approach to determine the influence of controlling factors on the output of the process [1-2]. It is a method to understand the cause and effect relationship of a certain process. DOE approach mainly consists on the following phases as mentioned below;
Planning Phase, This phase mainly deals with the activities based on problem statement, aims and objectives of experiments, development of experimental matrix with different levels of parameters and experimental setups to conduct experiments.
“It doesn't matter how beautiful your theory is, it doesn't matter how smart you are. If it doesn't agree with experiment, it's wrong”
Richard Feynman (Theoretical physicist)
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Execution Phase, This phase consists of execution of experiments and collection of all output variables required to judge the process.
Results and Analysis, in this phase results and conclusions have been drawn by analysing the experimental data sets. To validate the conclusions more experiments are required for confirmation.
3.2 Planning of Experiments Turning experiments were conducted under dry, conventional flood and vegetable oil based MQCL test conditions on a CNC turning center. In all of the appended publication, experiments were conducted using full factorial model [1-2]. In this thesis, the phases of DOE were employed as per the following steps;
1. Problem Statement: to investigate the machinability of Ti6Al4V using vegetable oil based MQCL system and to develop numerical models (FEA and CFD) for the prediction of energy consumption in machining and temperature distribution on the cutting tool.
2. Aims and Objectives: feasibility of vegetable oil based MQCL system as a potential alternative option to replace conventional flood cooling. Another aim was the development of numerical models (FEA and CFD) to facilitate sustainability in metal cutting sector by providing virtual assessment of energy consumption in cutting process and temperature distribution on the cutting tool.
3. Identification of Influencing Processing parameters: machining parameters (cutting speed, feed, depth of cut and cutting environment), tool wear, wear mechanisms, surface roughness, cutting forces, cutting temperature and power/ energy consumption. For the MQL and MQCL cooling strategies additional parameters such as flowrate, type of lubricant and nozzle orientation are also important.
4. Selection of Cutting Conditions: Select the appropriate and recommended machining parameters to develop the test
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matrix appropriate for the work-piece and cutting tool materials.
5. Development of Numerical Models: develop finite element and computational fluid dynamics based numerical models to predict influencing factors (cutting forces, cutting energy and cutting temperature distribution)
6. Execution of Experiments: execution of experimental setups and collection of the experimental data.
7. Results and Analysis: Analyze the data using the appropriate analysis techniques and interpret the results. At the same time some sets of experimental data will be used to validate numerical models.
3.2.1 Workpiece and Cutting Tool Materials
– alloy Ti-6Al-4V. Stock of Ti6Al4V material was available in the form of cylindrical rod. The chemical composition (wt. %) and mechanical properties of Ti6Al4V are mentioned in Table 3.1 and 3.2 respectively.
Table 3:1 Nominal chemical composition of Ti6Al4V
Element Wt. % Element Wt. %
H N C Fe
0.005 0.01 0.05 0.09
V Al Ti
4.40 6.15
Balance
Table 3:2 Mechanical properties of Ti-6Al-4V at room temperature
Properties Values Properties Values
Tensile strength Yield strength Elongation
993 MPa 830 MPa 14%
Poison ratio Modulus of elasticity Hardness (HRC)
0.342 114 GPa
36
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Table 3:3 Specifications of the turning cutting inserts [3]
Turning Cutting Insert
Coated Carbide:
1. TCMT 16 T3 04-MM 1105
- The substrate consists of a hard fine grained WC with 6% Co for high hot hardness and good resistance against plastic deformation. - The new thin Physical vapour deposition (PVD) TiAlN coating with excellent adhesion, also on sharp edges, guarantees toughness, even flank wear and outstanding performance in heat resistant super alloys. 2. CCMT 12 04 04-MM 1105 (Used for MQCL Based Experiments due to compatable shape) - The substrate consists of a hard fine grained WC with 6% Co for high hot hardness and good resistance against plastic deformation. - Physical vapour deposition (PVD) TiAlN coating
Uncoated Carbide:
3. TCMT 16 T3 04-KM H13A
- Combines good abrasive wear resistance and toughness for medium to rough turning of heat resistance steel and titanium alloys.
4. CCMT 09 T3 08-KM H13A
(Used for MQCL Based Experiments due to compatable shape) - The cutting insert used is an uncoated cemented carbide turning tool insert (Grade: H13A). This grade offers good toughness and abrasive wear resistance against Ti-alloys and other metals
The experimentation on turning setup was conducted using two types of cutting tools, coated and uncoated carbide inserts. Table 3.3 shows the general specifications of turning inserts. These cutting inserts were selected on recommendation of tool supplier (Sandvik [3]).
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3.3 Experimental Setups CNC turning center was used to perform all of the cutting experiments mainly for semi-finishing operation. Each test was repeated twice to reduce experimental errors. In order to monitor and measure the surface finish achieved during experiments a Mitutoyo roughness tester SJ 201P was employed. Mitutoyo tool maker microscope was used to measure the flank tool wear. Scanning electron microscopy was performed to study the major wear mechanisms. Kistler multi-channel dynamometer was utilized for measuring the cutting forces generated during the machining operations. Cryogenic setup, under liquid Nitrogen (LN2), was employed in external cooling delivery system. The nozzle was aimed at the cutting edge with 45 .
Table 3.4 shows the experimental setups used to investigate different aspects of machining performance with respect to the papers numbers assigned in section I.
Table 3:4 Experimental setups for the appended Papers
Paper II:
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Paper IV:
54
Paper V:
Paper VI:
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3.4 Description of Vegetable Oil Based Minimum Quantity Cooling Lubrication (MQCL) System The MQCL system shown in Figure 3.1a was capable of combining low temperature air (0 to -6C°) with vegetable oil based mist (MQCL) applied both in external and internal arrangement. Vegetable oil in MQCL was operated at different flow rate for both internal and external arrangement. In order to apply MQCL arrangement internally specially designed tool holder (SCLC R 2525 M12-EB) from Mircona was utilized during the present study. Figure 3.1b, c and d shows the tool holder used.
(a)
(d)
(b)
(c)
Figure 3:1Mircona tool holder to support internal delivery of MQCL system, (a) MQCL system, (b) Tool Holder, (c) Illustration of internal coolant delivery passage, (d) passage dimensions [4]
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Table 3:5 Properties of vegetable oil used in mist [5]
Properties Description
)
)
3
< 3%
The vegetable oil (ECULUBRIC E200L) was supplied by ACCU-Svenska AB. The vegetable oil used in this study was mainly based on rapeseed oil. The flow rate of mist was controlled by regulating the low temperature air and oil supply. The information about the vegetable oil is shown in Table 3.5.
3.5 Collection of Machining Variables The equipment used to measure machining variables is discussed in detail in the below section.
3.5.1 Measurement of Cutting Force
Cutting force data was measured and collected during the machining experiments. The cutting force data was collected in the Z (Fz), X (Fx) and Y (Fy) directions using Kistler [6] (Model: 9257b) dynamometer. In order to obtain signal multichannel (4) charge amplifier (Model: 5070) was used. The output signals from charge amplifier were presented on a personal computer using Dynoware software package.
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(a) (b)Figure 3:2 Cutting force data evaluation system, (a) Kistler 9257b Dynamometer, and (b) Kistler 5070 charge amplifier
Table 3:6 Specifications of Kistler dynamometer [6]
Model 9257b (Dynamometer) 5070 (Charge amplifier)
Calibration Range (kN)
Fx direction Fy direction Fz direction
Sensitivity (pC/N)
Fx direction Fy direction Fz direction
0-5 (kN) 0-5 (kN) 0-5 (kN) -7.916 -7.902 -3.707
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3.5.2 Measurement of Tool Wear and Wear Mechanisms
Flank wear appears on the tool due to the friction between machined surface and flank face of the cutting tool. The amount of flank tool wear was measured in the machining experiments using standard ISO 3685:1993 [7]. The maximum values of width of the flank wear land were measured during experiments. To investigate flank wear, Mitutoyo tool maker microscope [8] (Model: 510) was used in the presented research work. Figure 3.3 shows the Mitutoyo tool maker microscope.
Figure 3:3Mitutoyo tool maker microscope (Model: TM 510) Table 3:7 Specifications of Mitutoyo tool maker microscope [8]
Model Mitutoyo TM 510 XY range Effective area of table Max. workpiece height Max. workpiece weight Total magnification Transmitted illumination Reflected illumination
100x50mm 150x92mm 107mm 5kg 30X Light source: Tungsten bulb (24V, 2W) Green filter, Light intensity adjustable Light source: Tungsten bulb (24V, 2W) Light intensity adjustable
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After each turning experiment the cutting inserts were collected and cutting edges were marked with different colors, and placed in plastic bags that were marked with parameters like cutting speed, test number and cutting tool material. Figure 3.4 shows the TEKSCAN scanning electron microscope used to study the tool wear mechanisms. The micrographs of cutting inserts were obtained under hi-vacuum scanning modes. For the present study micrographs were taken using spot size of 3 and beam acceleration voltage as 30 kV.
(a)
(b)
Figure 3:4(a) Scanning electron microscope (TEKSCAN), (b) Sample SEM images of uncoated carbide tool under Dry, Vc = 120 m/min and f = 0.15 mm/ rev
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3.5.3 Measurement of Surface Roughness
The study utilized Mitutoyo surface roughness tester SJ 201P to measure the measure the arithmetical mean deviation of the profile (Ra). The study utilized the measurement procedure as suggested by the standard ISO 4287. Figure 3.5 shows the tester used for experimentation. Table 4.1 shows the general specifications of the surface tester used during experimentation. For all of the cutting experiments roughness values were measured three times and only average values are reported to reduce experimental errors.
Table 3:8 Specifications of Mitutoyo surface roughness tester [9]
Model Mitutoyo SJ 201P Drive speed Evaluation length Detecting method Stylus tip radius Measuring force Roughness standard Sampling length Display range Ra
0.25 mm/s, 0.5mm/s and 0.8 mm/s 12.5 mm Differential Inductance 5 m 4mN JIS, DIN, ISO, ANSI 0.25mm, 0.8 mm and 2.5 mm 0.01 – 100 m
Figure 3:5 Mitutoyo surface roughness tester SJ 201P
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3.5.4 Measurement and Calibration of Cutting Temperature
Cutting temperature was measured on the rake face of the tool using a standard K type thermocouple. The chip breaker was carefully clamped on the tool rake face under the optical microscope so that the hot junction was mechanically fixed at a specified distance from the cutting edge. The thermocouple (B) was used and fixed at known locations on the tool rake face. It was precisely mounted at known location from the cutting edge.
Figure 3:6 Schematic CAD illustration of calibration setup for thermocouples
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In order to achieve tool tip temperature during the cutting process, a calibration procedure is required for thermocouples. The experimental procedure provided by Kishawy [12] was replicated in this study. The procedure used two K type thermocouples, where thermocouple (A) was fixed at the tool tip and thermocouple (B) was fixed precisely at known location on the rake face of the cutting tool. The schematic illustration has been shown in the Figure 3.6.
3.5.5 Measurement of Cutting Power
In order to investigate the energy consumed during each cutting experiment, PS3500 power data logger [11] has been used to capture the power utilized during the cutting test. Table 3.9 show the specifications of power data logger used in experiments.
Table 3:9 Specifications of power logger
Model PS 3500
Operating Range Measurement Rate Frequency Measurement
0 - 50 degrees C, Relative humidity to 70% (non-condensing) Analyses two cycles per second of each
130 samples per cycle @ 60 Hz. All measurements updated once per second Range: DC, 45 - 66 Hz, 360 - 440 Hz fundamental, Display on meter: (PS3500 ONLY), Accuracy: ±0.5%
References [1] D. C. Montgomery, Design and Analysis of Experiments, 8th ed.
Wiley, 2012, p. 752. [2] AR Al Hassani, “Modeling of Tool Wear when Turning of TI-6AL-
4V Titanium Alloy,” American University of Sharjah, 2013. [3] Catalogue, “Cutting tools from Sandvik Coromant,” 2012. .
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[4] “Mircona AB, Tool holder (SCLC R 2525 M12-EB), Retrieved (12.03.2014) from the source; www.mircona.se/index_en.html .”
[5] A. S. AB, “ECOLUBRIC E200L, Material Safety Data Sheet.” [Online]. Available: http://www.accu-svenska.se/ecolubric-vegetablebased-lubricant.
[6] “‘Kistler Dynamometer Catalogue’. Internet: http://www.kistler.com/.”
[7] “ISO 3685, Tool life testing with single-point turning tools, ISO Standard, 3685 (1993) (E).”
[8] “Mitotoyo ToolMaker Microscope ‘http://www.mitutoyo.com/wp-content/uploads/2012/11/2122-TM505-510.pdf.’”
[9] “Surftest SJ-201P portable surface roughness tester , Accessed [20.08.2013], Available from: http://www.atecorp.com/ATECorp/media/pdfs/Mitutoyo-SJ-201P_Datasheet.pdf.”
[10] “Qualitest QV 1000 Catalogue ‘http://www.worldoftest.com/pdf/hardnesstesters.pdf.’”
[11] “Power Data Logger ‘http://www.summittechnology.com/Power_Analyzer_PS3500.html.’”
[12] H.A. Kishawy, “An Experimental Evaluation of Cutting Temperatures During High Speed Machining of Hardened D2 Tool Steel,” Mach. Sci. Technol., vol. 6, no. 1, pp. 67–79, May 2002.
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CHAPTER 4: NUMERICAL METHODOLOGY
This chapter presents the numerical approaches used to develop numerical models (Finite element and Computational fluid dynamic) for the prediction of energy consumption in machining and cutting temperature mapping on the tool.
This chapter provides detailed information about the numerical methods and techniques utilized during this research work. The chapter also provides detailed information about the steps performed to create CAD geometries, finite element (FE) and computational fluid dynamic (CFD) models.
4.1 Finite Element Machining Simulations
4.1.1 Introduction
Finite element simulations are very useful to predict the machining performance of a cutting process. Finite element approach is economically feasible method to develop understanding towards the metal cutting trends under specific set of cutting parameters. These simulations are preferred due to the following reasons;
FE simulations are capable of solving non-linear problems Advancement of many finite element software packages allow to couple thermo-mechanical analysis Unnecessary costly machining experimentation can be avoided Modifications and improvements in redesigning the tools are easier to conduct
“Until now, physical theories have been regarded as merely models which approximately describe the reality of nature. As the models
improve, so the fit between theory and reality gets closer” Paul Charles William Davies
(Physicist and writer)
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Significantly reduces the lead time for new tools
In this research, a finite element based software package DEFORM (Design Environment for Forming) was used. The DEFORM software create machining simulations using updated Lagrangian formulation method with implicit integration scheme to accommodate large deformation in simulation.
4.1.2 Modeling of the Cutting Tool Geometry
The cutting tool CAD geometry was created in AUTODESK Inventor professional as shown below in Figures 4.1. The geometries of cutting inserts have been presented in Table 4.1.
Table 4:1 Geometry of cutting inserts [1]
CCMT 12 04 04 MM 1105 TCMT 16 T3 04-KM H13A
L S IC Re
16.4978 mm 3.96875 mm
9.525 mm 0.4 mm
12.8959 mm 4.7625 mm
12.7 mm 0.4 mm
(a) (b)
Figure 4:1 (a) CAD model TCMT 16 T3 04-KM H13A,and (b) CAD model CCMT 12 04 04 MM 1105 [1]
In order to create the CAD based models of the cutting tools micro-geometric features on the insert was neglected. In the finite element
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based simulations, the cutting inserts were selected to be of plain Tungsten Carbide (WC) and TiAlN coated Tungsten carbide materials. The thermophysical properties of the plain Tungsten carbide tool material are available in the library of finite element package DEFORM. In order to simulate the cutting process, tool material was modelled as a rigid material neglecting the tool wear computation. The TiAlN coating material properties data is listed in Table 4.2.
Table 4:2 Temperature dependent mechanical and thermo-physical properties for TiAlN coating
Property TiAlN coated tool [2]
E(T) [MPa] C]
C] Cp(T) [N/mm2/ C]
6.0 x 105
9.4 x 10-6 0.0081T + 11.95 0.0003T + 0.57
4.1.3 Modeling of the Workpiece Material
As recommended by literature [2-3], a modified version of Johnson-Cook constitutive model was incorporated in the Deform 3D FE simulations. The modified Johnson – Cook model contains the effect of temperature based flow softening, strain, strain rate hardening and thermal softening [4]. The model is presented in Eq. (4.1). The additional term in model with tanh function is responsible of changing flow stress at higher strains to introduce the effect of flow softening. Parameters p, r, S and D are responsible of controlling the flow behaviour and depends upon the material itself. The exponent S specifically controls the tanh function for thermal softening [5].
n ( ( ))] [1+C ln °]
[1 – ( °°) ] [D + (1-D) [tanh (( ) ) ]] 4.1
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Where D=1 ( ) , p=( )T is workpiece
temperature, Tm is melting point and T° is ambient temperature.
Johnson-Cook parameters have been selected from the literature [2]. These parameters are specified in Table 4.3. Flow stress curves have been plotted against the true strain using different strain rate levels. These flow stress values have been incorporated in to the Deform 3D to simulate cutting behaviour of Ti6Al4V.
Figure 4:2 Flow stress curves using modified Johnson-Cook constitutive model [3] Table 4:3 Johnson-Cook model parameters [2]
A B C n m
782.7 MPa 498.4 MPa
0.028 0.28 1.0
S r d b a
0.05 2 1 5 2
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= m k 4.2
Equation 4.2 says that frictional stress on the rake face is proportional to the shear flow strength of the work material. Shear friction law is used to define the severe contact conditions in metal cutting. Where m
nd k is work material shear flow stress. Literature [2] used friction factor (m) value of 0.85 for TiAlN coated tools. At the remaining length of the cutting tool sliding region is formed. Sliding region means mild contact condition and can be modelled by using Coulomb friction law. Coulomb friction law is represented in Eq. (4.3).
4.3
stress. Literature [2] used coefficient of friction (μ) value of 0.5 for TiAlN coated tools.
The Cockroft and Latham damage criterion [9] was utilized in 3D finite element simulations to represent the fracture. The fracture criterion generally controls the segmented chip formation when machining titanium alloys [10]. As per this damage model, fracture starts when integral of highest principal stress component over a strain path becomes equal to the certain critical damage value. Eq. 4.4 represents the Cockroft and Latham damage model. The critical damage value was used as 0.6, which is a general value for machining simulations.
= 4.4
Where f is effective strain, 1is maximum principal stress and D is critical damage value based on material. The finite element model utilized the updated Lagrangian model formulation in combination with the automatic remeshing method. New mesh is generated when the software identifies element distortion, as shown in Figure 4.3.
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(a)
(b)
(c)
Figure 4:3 Remeshing as the cutting tool proceeds through the workpiece material (a) Step1 (b) Step 51 (c) Step 300
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4.2 Computational Fluid Dynamic Simulations The basics theory and governing equations of the computational fluid dynamic (CFD) approach has already been discussed in chapter 2 under section 2.6.
Table 4:4 Properties of uncoated carbide for the CFD model [8]
-3) -1 K -1)
Specific heat c (Jkg-1K-1) w (K-1)
15000 46 203 4.7 x 10 -6
4.2.1. Pre-Processing: Turbulence Model Selection
One of the most important challenges in the fluid flow problem is the simulation of turbulence. Turbulent flow occurs when flow experiences large fluctuations of velocity and pressure in space and time resulting in cross current of flow streams. There are different turbulence models available in the common CFD modeling software packages. The present study utilized shear stress transport (SST) turbulence model available in ANSYS® CFX. SST turbulence model was selected from the literature [13]where it was recommended to use SST for similar problems. SST turbulence model is a combination of k – applied near the walls and other k – ay from the walls. The model is robust and provides good acceptable results near the solid boundaries. SST was selected for the present problem because our main focus is on the cutting insert that is a solid domain.
4.2.2. Computational Domain and Boundary Conditions
The study involves the investigation of convection over a cutting insert with constant heat source inside a square enclosure. The computational domain of the presented CFD model consists of the fluid and solid domains. ANSYS® CFX was used to develop the geometry, mesh and solution of the problem.
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At solid – fluid interface, similar conditions of heat flux and temperature were solved using both fluid energy and solid energy equations. Figure 4.4 shows the development of computational domain using commercial software package ANSYS® CFX.
Figure 4:4 Illustration of computational domain
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Figure 4:5 Stream lines of air at inlet velocity 0.1 m/sec interacting with the heat source at cutting tool tip
The boundary conditions of the computational domain were set as follows: The air speed of free stream was assigned at the inlet boundary and the static ambient pressure was assigned at the outlet boundary. The symmetric boundary condition was applied to the all other four side walls. Non-slip boundary condition was applied on the interface of cutting insert.
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Figure 4:6 Computational domain for CFD model and meshing performed using CFX (ANSYS) mesh generator [13]
4.2.3. Meshing of Domains
The accuracy of simulated results, convergence and computational time is mainly controlled by the structure of mesh. A CFX mesh generator provides various meshing options to facilitate the researchers. The CFX documentation [13] is available to guide the researchers about the best practices when meshing computational domain. As a general approach for CFD projects, structured mesh is preferred due to its resource consumption and accuracy.
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Figure 4:7 CFX (ANSYS) Solver GUI for convergence achieved during our CFD simulations with residual target of 1.0e-6
4.2.4. Solver Execution
After the development of CFD model, solution step is initiated. Generally these CFD solvers use iterative approach to solve complex nonlinear flow based problems. In order to start iterative solving procedure initial arbitrary values of the flow velocity, flow pressure, flow temperature and other transport properties are required. Intelligent initial conditions play significant role towards the iterative procedure. If initial values are given intelligently computational time can be significantly reduced. An appropriate interpolation scheme is incorporated to control the solution. CFX solver (ANSYS) has some
75
commonly used interpolation schemes named as First-Order Upwind, Second-Order Upwind, Second-Order Central, and Quadratic Upstream Interpolation Convective Kinetics (QUICK) [13]. Once the calculations have been received from the model, solution is monitored by observing the convergence [13]. Convergence is the process of monitoring imbalances or residuals from the numerical calculation during each iteration step. Figure 4.7 shows the convergence achieved during one of the CFD model with residual target of 1.0e-6.
References [1] Catalogue, “Cutting tools from Sandvik Coromant,” 2012. . [2] T. Özel, M. Sima, a. K. Srivastava, and B. Kaftanoglu, “Investigations on the effects of multi-layered coated inserts in machining Ti–6Al–4V alloy with experiments and finite element simulations,” CIRP Ann. - Manuf. Technol., vol. 59, no. 1, pp. 77–82, Jan. 2010. [3] T. Özel, M. Sima, and A. K. Srivastava, “Finite element simulations of high speed machining Ti6Al4V alloy using modified material models,” Trans. NAMRI/SME, vol. 38, pp. 49–56, 2010. [4] T. Ozel, I. Llanos, J. Soriano, and P.-J. Arrazola, “3D Finite Element Modelling of Chip Formation Process for Machining Inconel 718: Comparison of Fe Software Predictions,” Mach. Sci. Technol., vol. 15, no. 1, pp. 21–46, Apr. 2011. [5] M. Sima and T. Özel, “Modified material constitutive models for serrated chip formation simulations and experimental validation in machining of titanium alloy Ti–6Al–4V,” Int. J. Mach. Tools Manuf., vol. 50, no. 11, pp. 943–960, Nov. 2010. [6] Y. Karpat, “Temperature dependent flow softening of titanium alloy Ti6Al4V: An investigation using finite element simulation of machining,” J. Mater. Process. Technol., vol. 211, no. 4, pp. 737–749, Apr. 2011. [7] T. Özel, “Computational modelling of 3D turning with variable edge design tooling: influence of micro-geometry on forces, stresses, friction and tool wear,” J. Mater. Process. Technol., vol. 209, no. 11, pp. 5167–5177,, 2009. [8] P. J. Arrazola and T. Özel, “Investigations on the effects of friction modeling in finite element simulation of machining,” Int. J. Mech. Sci., vol. 52, no. 1, pp. 31–42, Jan. 2010. [9] M. G. Cockroft and D. J. Latham, “Ductility and workability of metals,” J. Inst. Met., vol. 96, pp. 33–39, 1968.
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[10] K. S. V. Sekar and M. P. Kumar, “Finite Element Simulations of Ti6Al4V Titanium Alloy Machining to Assess Material Model Parameters of the Johnson-Cook Constitutive Equation,” vol. XXXIII, no. 2, pp. 203–211, 2011. [11] R. G. and J. V. John D. Anderson Jr., Joris Degroote, G´erard Degrez, Erik Dick, Computational Fluid Dynamics: An Introdcution,, 3rd ed. Springer-Verlag Berlin Heidelberg 2009, 2009. [12] A. Sayma, Computational Fluid Dynamic. Ventus Publishing Aps. [13] “ANSYS, Inc., ANSYS CFX Solver Theory Guide. USA; 2009..”
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SECTION IVRESEARCH OUTCOMES AND RESULTS
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CHAPTER 5: RESULTS AND DISCUSSION FROM EXPERIMENTAL STUDIES (PAPERS I - V)
This chapter presents the results from the machinability of Ti6Al4V based experimental studies. Most of the results came from appended published papers (I – V).
In this chapter main experimental results from the machinability focused research work have been presented.
5.1 Outcomes of Literature Review (PAPER I) The literature shows that many researchers have explored the machinability of Ti6Al4V using dry, conventional flood, mineral oil MQL, vegetable oil MQL, chilled air and liquid nitrogen as cooling strategies. The literature shows that although MQL is considered as strong option to replacing the conventional flood cooling, though MQL is mainly developed to facilitate the lubrication process instead of cooling process. Elevated heat generation during the machining of titanium alloy limits the performance of MQL technique due to missing component of cooling in it. There is a need to provide the missing component of cooling in MQL technique using hybrid cooling concepts.
“Never trust an experimental result until it has been confirmed by theory”
Sir Arthur Stanley Eddington (British Astrophysicist)
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Uncoated-carbide tools are referred as potential materials to machine titanium alloys at lower cutting speeds of 30–40m/min. It has been reported that K type uncoated tools performed comparatively better than CBN and other uncoated tools.
TAM of titanium alloys was reported as unsuitable approach in literature. Although it focuses on the thermal softening of the workpiece. Reduction in cutting force (15–50%) is observed when machining using this technique. It works fine in certain cutting speed range, but initiates diffusion wear at rapid rate due to the presence of high cutting temperature.
It has been reported that CVD-coated tools are more susceptible toward diffusion wear and catastrophic failure, because of loss in substrate integrity at high processing temperature in CVD method. PVD-coated tools maintain substrate integrity and offer more resistance toward diffusion wear.
Several studies showed that coated-carbide tools outclassed their uncoated equivalents when machining titanium- and nickel-based alloys. As majority of heat is transferred into the cutting tool material, it is recommended that both cutting tool and coating materials have thermal conductivity lower than the workpiece being machined. Lower thermal conductivity of tool material improves tool life. It has been observed that for coated carbide tool coating delamination starts at the beginning of machining operation and then the normal modes of abrasion, adhesion, and diffusion wear mechanisms appear.
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5.2 Experimental Results (Papers II – V) The experimental results section is based on the results obtained in the experimental studies where machining performance of MQCL cooling strategy was explored for the internal and external arrangement. In other study different flow rates under internal MQCL were investigated and the last study compared MQCL with various other sustainable cooling strategies. The results revealed encouraging potential of MQCL (Internal), and enlighten the possibility of replacing the conventional flood cooling. MQCL (Internal) performance was more evident towards higher cutting speeds. When different flow rates were investigated under MQCL (Internal), the study revealed the concept of optimum level of flowrate that controls the lubrication capacity and penetration ability of the cooling strategy. The study with other sustainable cooling strategies provided cryogenic (LN) and MQCL machining a favourable choice. Nevertheless life cycle analysis (LCA) of cryogenic (LN) with MQCL is required here because liquid Nitrogen (LN) generations consumes a lot of energy in extraction and production phase.
5.2.1 Comparison between Internal and External MQCL
a) At Cutting Speed of 90 m / min
Figure 5.1a shows the surface roughness attained using various cooling strategies at the cutting speed of 90m/ min. The first graph in Figure 5.1a shows the roughness at feed of 0.1 mm/ rev and cutting speed of 90 m/min. In order to understand it completely, Figures 5.1b, 5.2a and 5.3 should also be considered for flank tool wear, SEM micrograph and cutting force in combination. It can be observed that MQCL (Internal) provided better tool life and produced lowest cutting force, but the surface roughness was higher than flood and MQCL (External). The better tool life and lower cutting forces points out at the potential of MQCL (Internal). The higher surface roughness of MQCL (Internal) can be linked up with SEM micrograph of the tool which shows less adhesion but large traces of abrasion on the flank face. Dry cutting provided the worst machining performance as it generated less tool life, higher cutting force and higher roughness. SEM micrograph shows adhesion on the tool edge which indicates the presence of high cutting temperature on the edge.
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The second graph in Figure 5.1a provides surface roughness at feed of 0.2 mm/rev and cutting speed of 90 m/min. The results of Figures 5.1b and 5.2b should also be combined to observe the behaviour of cutting force and tool wear. Flood environment outclassed all other strategies pointing out that cutting temperature was removed efficiently. MQCL (Internal and External) both performed equally and can be referred as second best.
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250
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350
400
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0.1 0.15 0.2 0.25 0.3
Cutti
ng Fo
rce (
N)
Feed (mm/ rev)
Dry - 90 Flood - 90MQCL (External)90 MQCL (Internal)90
(b)
Figure 5:1 (a) Surface roughness observed under dry, MQCL (Internal), MQCL (External) and flood cooling strategies, (b) Comparison of cutting force with respect to feed
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(a) (b)
(c)
a)
Figure 5:2 Flank wear measurement at cutting speed of 90 m/ min, (a) feed = 0.15 mm/ rev, (b) feed = 0.20 mm/ rev and (c) feed = 0.25 mm/ rev
Figure 5.1a third graph shows the plots for surface roughness for all cooling strategies at cutting speed of 90 m/ min for feed of 0.25mm/ rev. The results of 5.1a, 5.1b and 5.2c were checked in combination. The observation shows that fluid cooling performed relatively better than other cooling methods. One reason of the flood better performance over dry and MQCL strategies is due to the reason that at higher feed level flood is able to penetrate the cutting zone and take away the heat efficiently. Whereas for the bad performance of dry and both MQCL (Internal) and MQCL (External) at higher feedrate the reason can be attributed to the high amount of heat generation due to higher material removal rate. This results in higher temperature at
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tool-chip interface thus increasing the tool wear and increasing the surface roughness.
Figure 5:3 SEM images of the tool wear at cutting speed of 90 m/min and feed of 0.15 mm/ rev
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b) At Cutting Speed of 120 m / min
The first graph in Figure 5.4a shows the roughness at feed of 0.15 mm/ rev and cutting speed of 120 m/min. In order to understand it completely, Figures 5.4b, 5.5a and 5.6 should also be considered for cutting force, flank tool wear, SEM micrograph for wear mechanisms in combination. The tool wear shows that better tool life was attained during MQCL (Internal) arrangement. The cutting forces were found in close difference to each other. However, surface roughness was found better for MQCL (External). It is important to know the lubrication method difference between MQCL (Internal) and (External).
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ng Fo
rce
(N)
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Dry - 120 Flood - 120
MQCL (External)120 MQCL (Internal)120
(b)
Figure 5:4 (a) Surface roughness observed under dry, MQCL (Internal), MQCL (External) and flood cooling strategies, (b) Comparison of cutting force with respect to feed
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(a) (b)
(c)
a)
Figure 5:5 Flank wear measurement at cutting speed of 120 m/ min, (a) feed = 0.15 mm/ rev, (b) feed = 0.20 mm/ rev and (c) feed = 0.25 mm/ rev
The efficiency of lubricant to get stacked to the surface is mainly dependent on the viscosity and frictional properties. When lubrication is provided in the tool – chip interface during the metal cutting, the flow of chip tends to move away the film of lubricant from the tool-chip sliding zone. This is the case of external arrangement of MQCL, however internal arrangement of MQCL provides passages through the cutting tool to lubricate the tool-chip interface. The SEM micrograph for the wear examination for MQCL (Internal) provides the reason for giving less surface finish then then MQCL (External). For MQCL (Internal) being the second best in term of surface finish can be attributed with the presence of notch at the cutting edge. Notch wear
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appears due to excessive localized failure at both flank and rake faces simultaneously. It appears as a single notch formation in start and then it grows into gross fracture with the passage of time. In case of dry machining edge chipping, abrasion and BUE was as major tool wear mechanisms.
Figure 5:6 SEM images of the tool wear at cutting speed of 120 m/min and feed of 0.15 mm/ rev
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In case of MQCL (external) machining, abrasion and adhesion assisted BUL were found as dominant wear mechanisms. In flood cutting, chipping at the cutting edge and abrasion were found as major wear mechanisms. However, adhesion was also found at a small scale.
The second graph in Figure 5.4a shows the roughness at feed of 0.2 mm/ rev and cutting speed of 120 m/min. In order to understand it completely, Figures 5.4b, and 5.5b should also be considered for cutting force and flank tool wear. At this condition flood environment outclassed others in terms of cutting force and tool life but surface roughness was slightly on higher side. One explanation of this can be that tool life is taken as a width of wear band but roughness is mainly associated with the condition of the edge. In this case the cutting edge distortions can be reflected on the finished surface. The cutting forces were found in close difference to each other besides flood. It can be noted here that MQCL (External) and dry behaved almost similar by giving similar cutting forces, lower tool life and higher roughness which can be an indication of BUE presence due to the presence of high temperature in the cutting zone.
The third graph in Figure 5.4a shows the roughness at feed of 0.25 mm/ rev and cutting speed of 120 m/min. In order to understand it completely, Figures 5.4b, and 5.5c should also be considered for cutting force and flank tool wear. It can be observed that due to the generation of high heat at higher feed all cooling techniques behaved very closely. The MQCL (Internal) technique was found relatively superior as it provided better tool life and surface finish. It points out at the sufficient effort provided by the lubrication method in order to reduce heat generation at the cutting interface.
c) At Cutting Speed of 150 m / min
The first graph in Figure 5.7a shows the roughness at feed of 0.15 mm/ rev and cutting speed of 150 m/min. In order to understand it completely, Figures 5.7b, 5.8a and 5.9 should also be considered for cutting force, flank tool wear, SEM micrograph for wear mechanisms in combination. The MQCL (Internal) outclassed others in this condition by providing better finish, low forces and approximately similar tool life to what attained in flood. The SEM micrograph shows edge
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chipping assisted gross fracture at the cutting edge in dry cutting. Adhesion and microchipping were also observed as a major tool wear mechanisms in case of dry machining. The integrity of cutting edge was found in better condition for MQCL (Internal). Adhesion assisted BUL and abrasion were found as major wear mechanisms in case of both internal and external MQCL cutting strategies. However the extent of BUL was found large in case of MQCL (external) arrangements. In flood cutting in addition to the BUL and abrasion, microchipping was also observed.
(a)
(b)
Figure 5:7 (a) Surface roughness observed under dry, MQCL (Internal), MQCL (External) and flood cooling strategies, (b) Comparison of cutting force with respect to feed
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(a)
(c)
(b)
Figure 5:8 Flank wear measurement at cutting speed of 150 m/ min, (a) feed = 0.15 mm/ rev, (b) feed = 0.20 mm/ rev and (c) feed = 0.25 mm/ rev
The performance of MQCL (Internal) was also found encouraging when results were checked for higher feed levels of 0.20 and 0.25 mm/ rev for the same cutting speed of 150 m/ min. The better performance of MQCL (Internal) at higher cutting speed was also found in accordance with the available literature [1]. One reason of this better performance can be attributed with the tool-chip contact length in machining. The contact length has a controlling influence on the heat generation at the secondary deformation zone. The higher cutting speed tends to reduce the contact length the literature also support
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that MQL has also a tendency to reduce the contact length [2]. Another study [3] also points out the relevance of chip curl with respect to the contact length. The increase in heat removal from the chip can result in more curling and hence reduction in the contact length.
Figure 5:9 SEM images of the tool wear at cutting speed of 150 m/min and feed of 0.15 mm/ rev
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The study provided the results, which shows that MQCL (Internal) and MQCL (External) strategies behave differently under very close cutting parameters making it difficult to provide a generalized statement. However, some common observations have been listed and possible causes of deviations are also provided with the help of available literature.
The influence of the MQCL (Internal) application method on overall machining performance was more evident at high cutting speed of 150 m/min. One reason of it can be due the short contact length. This is also in accordance with what found in literature [1]. For the cutting speeds of 90 and 120 m/min, cutting forces were found very close to each other and in some cases cutting forces with MQCL (Internal) and (External) were found larger than in the dry cutting. One reason for this can be related to the presence of less heat. It means that workpiece material was heated less and maintained its hardness. In other words thermal softening was reduced resulting in higher cutting force as pointed out in literature[3]. As found in literature [4], for machining Ti6Al4V the relation between contact length and cutting speed is different than conventional steels. The study shows that contact length increases first and then decreases in different regimes of cutting velocity and feed. The phenomenon of adiabatic shear banding in Ti6Al4V machining is attributed as main reason of this diverse behaviour of contact length. This different nature of contact length can also be a reason of diverse nature of results obtained at lower and higher level of cutting speeds.
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5.2.2 Comparison between different flowrates in Internal MQCL
(a) At Cutting Speed of 90 m/ min
The surface roughness results were plotted in term of average, maximum and minimum values for 90 m/ min cutting speed and feed levels as shown in the Figures 5.10. It represents the surface roughness attained for dry, MQCL (60, 70, 80, 90 and 100 ml/h) and flood cooling strategies. The lower graph in the Figure 5.10 represents feed level of 0.1 mm/ rev. To analyse it completely, Figures 5.11 and 5.12a should also be considered for cutting forces and tool wear.
Figure 5:10 Surface roughness results at feed levels from 0.1 – 0.3 mm/ rev and Cutting speed = 90 m/ min
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Figure 5:11 Comparison of cutting force under different cooling strategies at cutting speed of 90 m/ min
The feed 0.1 mm/rev points out at the better performance of MQCL (80ml/h) with respect to the surface roughness. But more tool life was found for flood and MQCL (70ml/hr) was the second best. The tool life generally considers the wear band but condition of cutting edge is also critical here. Better surface roughness in MQCL (80ml/h) points out the cutting edge integrity was comparatively better. The cutting forces were found in close relevance to flowrates (60 – 90 ml/hr). Higher cutting force was observed for flood environment, it was even more than dry environment. This can be explained with the phenomena that more cooling of workpiece helps in maintaining higher hardness and reduction in thermal softening due to which higher cutting force generates. For the feed of 0.2 mm/rev lower values of surface roughness and cutting forces were observed for MQCL (80 – 100 ml/h). The best tool life was found for flood and MQCL (60ml/h) was the second best. The rest of the strategies were found in very minute difference to each other. The amount of material adhered to the cutting tool tip is mainly controlled by the lubrication/ cooling strategies. The material adhered in all strategies but the amount of the adhered
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material varied with the type of coolant environment. This adhered material on the tool tip also forms built-up-edge (BUE) that has a powerful influence on the surface roughness.
For the feed of 0.3 mm/rev the results for surface roughness, cutting forces and tool wear were found better for MQCL (100 ml/h). MQCL (100 ml/h) better performance at higher feed higher can be attributed with better lubrication. However, cutting forces for all strategies were found in close difference to each other.
(a) (b)
(c)
(b)((
Figure 5:12 Flank wear measurement at cutting Speed 90 m/ min (a) feed of 0.1 mm/ rev (b) feed of 0.2 mm/ rev, (c) feed of 0.3 mm/ rev
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(b) At Cutting Speed of 120 m/ min
The surface roughness results were plotted in term of average, maximum and minimum values for 120 m/ min cutting speed and feed levels as shown in the Figures 5.13. It represents the surface roughness attained for dry, MQCL (60, 70, 80, 90 and 100 ml/h) and flood cooling strategies. The lower graph in the Figure 5.13 represents feed level of 0.1 mm/ rev. To analyse it completely, Figures 5.14, 5.15b and 5.16 should also be considered for cutting forces, tool wear and wear mechanisms.
Figure 5:13 Surface roughness results at feed levels from 0.1 – 0.3 mm/ rev and Cutting speed = 120 m/ min
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The surface roughness and cutting force results for MQCL (70ml/h), MQCL (90 ml/h) and MQCL (100 ml/h) were found very close to each other and comparatively better than others. However the tool wear results show that MQCL (70ml/h) provided the better tool life. The better results of MQCL (70ml/h) points out that it is an optimum value of flowrate for this condition. Figure 5.16 shows the SEM micrographs of tool wear at flank face of the tool under cutting speed of 120 m/min and feed of 0.1 mm/ rev using dry, MQCL (60, 70, 80, 90 and 100 ml/h) and flood cooling strategies. It can be observed that adhesion and abrasion wear mechanisms were found as major wear mechanisms in the cutting tools on almost all cutting cases.
Figure 5:14 Comparison of cutting force under different cooling strategies at cutting speed of 120 m/ min
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(a) (b)
(c)
Figure 5:15 Flank wear measurement at cutting Speed 120 m/ min (a) feed of 0.1 mm/ rev (b) feed of 0.2 mm/ rev, (c) feed of 0.3 mm/ rev
In the dry cutting, abrasion and adhesion wears were evident and presence of built-up-edge (BUE) due to excessive heat can also be observed. Edge-chipping was also seen at the cutting edge. Presence of BUE on the cutting edge can be a possible cause of high surface roughness in dry cutting. Comparatively less abrasion and adhesion was observed at flood environment, but edge chipping was quite visible on the edges that can be one of the reasons of slightly higher surface roughness. In case of MQCL machining abrasion wear, adhesion wear, BUE formation and edge chipping were observed in almost all of the MQCL oil flow rates.
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Figure 5:16 Wear mechanisms at feed 0.1 mm/ rev and cutting speed of 120 m/ min
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The cutting tool flank faces for MQCL (70 ml/h) and flood environment were found comparatively stable and in accordance with tool wear results. These points out that MQCL (70ml/h) served as optimal value at this condition.
For feed of 0.2 mm/rev MQCL (80 ml/h) provided better surface finish and cutting forces were also found slightly lower than other strategies. An interesting observation was found with respect to the tool wear result, where MQCL (80 ml/h) did not provide the best tool life against the expectations. The literature [5] explains such case by the argument, that increasing workpiece hardness can be a possible cause of high tool wear and better surface finish.
For feed of 0.3 mm/rev, results for surface finish and cutting forces were found comparatively better for MQCL (70 ml/h) and MQCL (100 ml/h). The tool wear results provided highest tool life for MQCL (70 ml/h). Figure 5.17 shows the SEM micrographs of tool wear at flank face of the tool under cutting speed of 120 m/min and feed of 0.3 mm/ rev using dry, MQCL (60, 70, 80, 90 and 100 ml/h) and flood cooling strategies. Abrasion wear was found very evident in almost all of the cutting cases. Similar to the previously discussed cutting condition, the cutting tool flank faces for MQCL (70 ml/h) cooling environment was found comparatively stable and in accordance with tool wear results. At this higher feed level of 0.3 mm/ rev, dry cutting and low oil flow rate of MQCL (60 ml/ h) resulted in excessive abrasion wear. In case of flood machining, edge chipping mechanism was observed. The better overall performance of MQCL (70 ml/h) shows the optimum level of flowrate for this cutting condition. It shows that appropriate level of flowrate with lower amount of oil can provide sufficient lubrication capacity and penetration ability.
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Figure 5:17 Wear mechanisms at feed 0.3 mm/ rev and cutting speed of 120 m/ min
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(c) At Cutting Speed of 150 m/ min
The surface roughness results were plotted in term of average, maximum and minimum values for 150 m/ min cutting speed and feed levels as shown in the Figure 5.18. It represents the surface roughness attained for dry, MQCL (60, 70, 80, 90 and 100 ml/h) and flood cooling strategies. The lower graph in the Figure 5.18 represents feed level of 0.1 mm/ rev. To analyse it completely, Figures 5.19 and 5.20a should also be considered for cutting forces and tool wear. At low feed of 0.1 mm/rev MQCL (100 ml/h) provided better surface finish, comparatively lower cutting force. The tool wear results were found very close to each other showing that higher cutting speed of 150 m/min brings the process towards aggressive cutting conditions.
Figure 5:18 Surface roughness results at feed levels from 0.1 – 0.3 mm/ rev and Cutting speed = 150 m/ min
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Figure 5:19 Comparison of cutting force under different cooling strategies at cutting speed of 150 m/ min
At higher feeds of 0.2 and 0.3 mm/rev, the performance of MQCL (70 ml/h) and MQCL (100 ml/h) was found comparatively better with respect to the surface finish and cutting force results. However, MQCL (70ml/h) provided better tool life as compared to other strategies. This can be linked again linked with optimal flowrate for the cutting condition. The tool wear results also show that MQCL results were found better than flood environment. One reason can be attributed to the MQCL delivery on the rake face, however in case of flood cooling cutting fluid cannot reach the rake face at tool-chip interface. The study provided an insight that increasing the amount of lubricating oil in MQCL does not simply improve the machining performance. The study showed that there is a concern of optimal level in flowrate. The possible explanation of this optimal level can be attributed with the lubrication capacity and associated penetration of the lubricating oil in MQCL arrangement. Another observation was made related to the viscous property of the lubricating oil. Mixing low temperature air with lubricating oil not only
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acts like a coolant but also increases the viscosity of the oil making it more viscous. It is definitely helpful with respect to the increasing lubrication capacity of the MQCL system, but there is also an issue of penetration with it. More viscous oil is difficult to penetrate in the cutting zone.
(a) (b)
(c)
)
( )
Figure 5:20 Flank wear measurement at cutting Speed 150 m/ min (a) feed of 0.1 mm/ rev (b) feed of 0.2 mm/ rev, (c) feed of 0.3 mm/ rev
As found in literature [1], MQL arrangement is generally recommended for higher cutting speeds. The better performance of MQCL (100 ml/h) at higher feed rates points out the potential of higher flowrates to be examined for higher feeds as well. Machining performance of MQCL cooling strategies was encouraging as in most cases the tool life was found close to flood strategy or sometimes even better.
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5.2.3 Comparison between different lubrication techniques
The effect of changing parameters on the surface roughness using various lubrication techniques is illustrated in Figure 5.21. It can be seen that at low feeds the surface roughness is much less and increases slightly upon using higher speed for same feed. Flood cooling appears to be the lubrication of choice when using low feed but not for high feed with cryogenic machining giving slightly better results. At high speed, the use of cooled air as lubricant emerges as the better alternative. However, at low speed, it does not yield good surface finish. It is also visible that in low feed cases, the surface roughness achieved through all techniques are close to each other, a trend not found at high feed. Moreover, an important point to note is the promise shown by both MQL and MQCL using vegetable oil over all.
Figure 5:21 Surface roughness with respect to different lubrication where v1 = 90m/min, v2 =120m/ min, f1 = 0.1mm/rev and f2=
0.2mm/rev
High surface roughness was observed at higher feed of 0.2 mm/rev for levels, 90 and 120 m/min, of cutting speeds. It is because surface
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roughness is directly associated with feed. At feed of 0.2 mm/rev, surface roughness was found higher at low cutting speed of 90 m/min. It means higher cutting speed of 120 m/min resulted in better surface finish when operated for higher feed of 0.2 mm/rev.
Figure 5:22 Flank tool wear with respect to different lubrication v1 = 90m/min, v2 =120m/ min, f1 = 0.1mm/rev and f2=
0.2mm/ rev
A closer look at each setting tells us that at low speed and feed, which is the representative setting for the insert and the study at hand, the best performance in terms of lowest flank wear of the tool is given by the MQCL with vegetable oil as in Figure 5.22. It was observed that unless the speed and feed are both at their higher levels, the tool wear for all techniques remains below or close to 0.3 mm. Figure 5.22 is a bar chart showings these effects. When vegetable oil MQL tool wear measurements were compared with the flood cooling strategy, it was observed that tool wear was decreased by 2.9 percent at 0.1 mm/rev and 90 m/min. Whereas vegetable oil MQCL and cryogenic lubrication
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both yielded a 15.4 percent reduction in tool wear. Hence at this feed and speed, cooled vegetable oil emerged as a sustainable alternative along with cryogenic machining. But literature [6] also points out the inconsistency in surface integrity in cryogenic machining as it is highly dependent on the pairing of tool and workpiece materials.
Figure 5:23 SEM micrograph of f = 0.1 mm/rev and v = 90 m/min
However, at 0.2 mm/rev and 90 m/min, 0.1 mm/rev and 120 m/min and at 0.2 mm/rev and 120 m/min, only cryogenic machining is the
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suitable alternative offering a 9.3 percent and .8 percent increase and 9.2 percent decrease in tool wear respectively. There are several studies [7-8]which show the better performance of cryogenic machining but the issue is related to the inconsistency of surface integrity of machined surface. The inconsistency varies with respect to the paring of tool and workpiece materials. Literature [9] also shows rapid increase in the strength and hardness of Ti6Al4V when machined under cryogenic arrangements. At the same time small variation in ductility and toughness was observed with respect to the dropping temperature.
At a speed of 90 m/min which is close to recommended high speeds for this titanium alloy and a feed of 0.1mm/rev, abrasion was seen as shown in Figure 5.23. The black particles deposited in some cases turned out to be carbon through multiple EDS runs hence indicating formation of small amounts of titanium carbide through diffusion. The EDS observations of the black spots were also found in accordance with literature [10]. In dry machining, larger abrasion marks were observed along with some built-up edge formation (BUE). In case of cryogenic machining, apart from a little carbon spots probably due to diffusion, relatively smaller abrasion was observed. Flood cooling yielded a classic case of abrasion to a lesser extent along with small adhesion resulting in a little built-up edge (BUE). Cooled air yielded a little more abrasion with small BUL formation. In case of vegetable oil MQL, a very small amount of chipping or attrition was spotted on the edge. The MQCL with the vegetable oil yielded very small abrasion along with a BUE.
At a higher feed of 0.2 mm/rev with the same speed, the amount of BUE and BUL visibly increased. The thickest BUE was witnessed in case of dry machining, while the least was observed in cryogenic machining again with very few dark spots which could also be seen in dry and vegetable oil MQL specimen. The only case of visible chipping or perhaps attrition was observed in the case of cooled air Figure 5.24.
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Figure 5:24 SEM micrograph of f = 0.2 mm/rev and v = 90 m/min
Moreover, at lower feed but a higher speed of 120 m/min, the extent of flank wear increased as a whole and abrasion was visible in all cases. The maximum abrasion along with the BUL was this time seen in both vegetable oil configurations. The few diffusion spots were spotted in all cases this time. Cryogenic machining, flood lubrication and cooled air showed lesser abrasion and thinner BUL as shown in Figure 5.25.
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Figure 5:25 SEM micrograph of f = 0.1 mm/rev and v = 120 m/min
At high feed and speed (0.2 mm/rev and 120 m/min), the extent of flank wear increased drastically. Dry machining yielded excessive, heavy chipping indicating gross failure whereas both vegetable oil cases yielded thick BUE’s with diffusion marks. Cooled air showed thick but smooth BUL formation, again with high wear and abrasion along with small indications of micro-chipping. Flood cooling yielded a BUL with diffusion marks too, but cryogenic machining showed a relatively smaller BUE along with lesser abrasion.
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The study investigated different cooling strategies for machining Titanium alloy (Ti6Al4V). The often proposed use of vegetable oil as a machining lubricant in both MQL and MQCL configuration was tested in Titanium (Ti6Al4V) turning. It was observed that it really has a possibility to be a sustainable alternative to synthetic cooling in terms of tool wear and surface roughness. Although flood cooling technique is the more prevalent practice in the industry these days, this study shows that it is feasible to use the sustainable alternative of vegetable oil.
The application of cooled air as a lubricant also appeared to be effective when compared with dry machining. Moreover, following the trends shown in literature, cryogenic machining also turned out to be a recommendable alternative by outperforming flood cooling, even at higher feed and speed. However, the cost to effectiveness extent needs to be further investigated between cryogenic and vegetable oil booster system. It would, however, be appropriate to further execute this study using a coated carbide grade because, while the aim was to push for challenging machining scenarios, the higher speed selected might be a little more than an uncoated tool can deal with. It was observed that adhesive and abrasive wear mechanism was dominant at flank face. However, diffusion and micro-chipping were also observed along with the formation of BUE and BUL.
References [1] A. R. Machado and J. Wallbank, “The effect of extremely low
lubricant volumes in machining,” Wear, vol. 210, no. 1–2, pp. 76–82, Sep. 1997.
[2] B. Tasdelen, H. Thordenberg, and D. Olofsson, “An experimental investigation on contact length during minimum quantity lubrication (MQL) machining,” J. Mater. Process. Technol., vol. 203, no. 1–3, pp. 221–231, Jul. 2008.
[3] L. Pejryd, T. Beno, and M. Isaksson, “Machining aerospace materials with room-temperature and cooled minimal-quantity cutting fluids,” pp. 74–86, 2010.
[4] S. A. Iqbal, P. T. Mativenga, and M. a. Sheikh, “A comparative study of the tool–chip contact length in turning of two engineering alloys for a wide range of cutting speeds,” Int. J. Adv. Manuf. Technol., vol. 42, no. 1–2, pp. 30–40, Jul. 2008.
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[5] T. Özel and Y. Karpat, “Predictive modeling of surface roughness and tool wear in hard turning using regression and neural networks,” Int. J. Mach. Tools Manuf., vol. 45, no. 4–5, pp. 467–479, Apr. 2005.
[6] A. Shokrani, V. Dhokia, and S. T. Newman, “Environmentally conscious machining of difficult-to-machine materials with regard to cutting fluids,” Int. J. Mach. Tools Manuf., vol. 57, pp. 83–101, Jun. 2012.
[7] G. Rotella, O. W. Dillon, D. Umbrello, L. Settineri, and I. S. Jawahir, “The effects of cooling conditions on surface integrity in machining of Ti6Al4V alloy,” Int. J. Adv. Manuf. Technol., vol. 71, no. 1–4, pp. 47–55, Nov. 2013.
[8] K. A. Venugopal, S. Paul, and a. B. Chattopadhyay, “Tool wear in cryogenic turning of Ti-6Al-4V alloy,” Cryogenics (Guildf)., vol. 47, no. 1, pp. 12–18, Jan. 2007.
[9] S. Y. Hong and Y. Ding, “Cooling approaches and cutting temperatures in cryogenic machining of Ti-6Al-4V,” Int. J. Mach. Tools Manuf., vol. 41, no. 10, pp. 1417–1437, Aug. 2001.
[10] M. Armendia, a. Garay, L.-M. Iriarte, and P.-J. Arrazola, “Comparison of the machinabilities of Ti6Al4V and TIMETAL® 54M using uncoated WC–Co tools,” J. Mater. Process. Technol., vol. 210, no. 2, pp. 197–203, Jan. 2010.
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CHAPTER 6: RESULTS AND DISCUSSION FROM NUMERICAL STUDIES (PAPERS VI - X)
This chapter presents the results from the FE and CFD based numerical studies conducted to enhance the knowledge towards machining of Ti6Al4V. Most of the results came from appended published papers (VI – X).
In this chapter, main numerical results obtained from the finite element modeling (FEM) and computational fluid dynamic (CFD) modeling based research works have been presented.
6.1 Finite Element Machining Simulation (Papers VI – VII) Papers (VI – VII) are concerned with the experimental and numerical investigation of energy consumption involved in the turning of Ti6Al4V titanium alloys. Energy consumption of a machining process is considered as an important machining performance indicator. These papers propose an approach for the prediction of energy consumption and related environmental implications using finite element modeling (FEM) simulations.
The methodology adopted to conduct the research was based on the utilization of finite element modeling based machining simulations using Deform 3D software to calculate energy consumption. The finite element model was used to predict the cutting forces in machining process. The simulated cutting forces were utilized to calculate power and productive energy portion involved in the cutting operation. On the other hand non-productive component of the energy was
“Learning by doing, peer-to-peer teaching, and computer simulation are
Nicholas Negroponte (Greek American Architect)
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calculated by measuring the power and energy consumption involved in air or dry cutting. The schematic illustration of proposed approach has been displayed in Figure 6.1.
Figure 6:1 Schematic flow diagram of proposed methodology
6.1.1 Simulated and Experimental Cutting Forces
The simulated cutting forces are illustrated in Figure 6.2 from paper VI. From paper VI the experimental cutting forces are reported and compared with the simulated cutting forces as shown in Figure 6.3. In Figure 6.3 no substantial increase in the magnitude of cutting force was recorded with increasing cutting speed. However a slight decrease in the cutting force was observed in the cutting forces. Increase in the cutting speed results in the generation of high temperature in the cutting zone. Thermal softening phenomenon can be attributed as a reason of stable cutting forces at higher levels of cutting speeds under dry condition.
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Figure 6:2 Finite element simulated cutting forces and temperature fields for feed rate 0.1 mm/ rev, Cutting speed (Vc) of 120 m/ min
Figure 6:3 Comparison of experimental and simulated cutting forces
The experimental and simulated cutting forces were found in good agreement with each other. The finite element simulations predicted cutting forces with an error in the range of 3.4 - 7.0%.
Similarly good experimental and numerical comparison of cutting force was obtained in paper VIII as shown in Figure 6.4. The simulated cutting forces were found in good agreement with experimental results
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with about 6% prediction error. However, at 0.3 mm/ rev feed level cutting force predictions showed an error of 1- 15%.
Figure 6:4 Comparison of experimental and simulated cutting forces feed of 0.1 mm/ rev
6.1.2 Simulated and Experimental Energy Consumption
The total energy consumption of a machining process can be mainly divided into the energy consumed in the core cutting phase and idle energy consumed in running the machine modules at zero load. Equation (6.1) shows the total energy consumption of a machining operation as discussed above.
E Total = E cutting + E idle 6.1
The cutting force (Fc) in the orthogonal machining model has same direction as of the cutting speed (Vc) and can be used to calculate power involved in the cutting action. Energy involved in the cutting action can be calculated by taking the product of time involved in the cutting action. Eq. (6.2) shows the energy consumed in the cutting process.
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E cutting = Pcutting Simulated Vc 6.2
Figure 6:5 Comparison of energy consumption between experimental and proposed method
In order to estimate the idle energy (E idle) involved in a machining process at certain cutting conditions, power has been captured for air cutting (dry run) using a power logger PS 3500. The total energy consumed in the machining was predicted by combining the energy in cutting using simulated cutting forces and energy consumed by machine tool during air cutting (dry run). Figure 6.5 shows the comparison of energy consumption using proposed method and
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experimental measurements. The proposed methodology was also validated experimentally by capturing the power and energy consumption in the actual machining at specified levels of cutting
cutting speed after analyzing the cutting duration with the help of power signal using power logger.
For the present study machining length (60 mm) and cutting speeds (90, 120 and 150 m/min) were kept constant due to which variation in machine speed was obtained by workpiece diameter. Variation in the diameter resulted in different machining times for different cutting speeds. The experimental work and proposed method to estimate energy consumption were found in good agreement with each other. The proposed method predicted energy consumption of machine tool with an error in the range of 1 - 8 %.
Table 6:1 Lifecycle estimates of gCO2e/ kWh for electricity generation procedures [1]
Technology Capacity/ Configuration/ Fuel Estimates gCO2e/ kWh
–
443 664 778 778
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6.1.3 Estimation of CO2 Emissions
Table 6.1 consists of the data related to the CO2 emissions (e/kWh) produced in the life cycle of electricity generation using different types of energy sources. As in United Arab Emirates most of the electricity is generated from fossil fuels, so the presented study has used the value of 778 gCO2e/ kWh in order to estimate CO2 emissions from computed energy consumption. The calculated values of CO2 emissions (e/kWh) were found 38.00, 37.02 and 29.65 (gCO2e/kWh) for the cutting speeds of 90, 120 and 150 m/ min respectively.
6.2 Computational Fluid Dynamic Simulation (PAPERS VIII – X)The methodology, implemented in this CFD based study; consist of two separately conducted finite element based simulations. In the first phase, conventional finite element machining simulation was conducted using appropriate material constitutive model, friction rule and fracture law. This modeling phase was executed using Deform 2D/ 3D software package. Cutting temperatures generated in the cutting zone, shear plane and tool tip were obtained during this first part of simulation phase. In the second phase the highest temperature value obtained at the tool tip was used as heat source and placed at the nose of the cutting tool geometry. ANSYS CFX was employed in the second phase where thermal interaction of cutting tool with heat source and air as a cooling media was simulated. The simulated results show encouraging results.
6.2.1 Phase 1: Cutting Temperature Predictions Using FEA
All papers (VIII – X) deal with the estimation of cutting temperature using FEA simulations. Paper VIII used 2D FEA machining simulation as shown in the Figure 6.6a. However, Paper IX used 3D FEA machining simulations as shown in Figure 6.6b. Both studies utilized modified
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version of Johnson Cook Equation as described earlier in chapter 4. The influence of using modified Johnson Cook equation can be seen on the chip formation in 2D machining case.
(a)
(b)
Figure 6:6 Finite element simulated cutting temperature at Cutting speed (a) Speed of 150 m/ min and feed of 0.2 mm/ rev, (b) feed rate 0.3 mm/ rev, depth of cut 0.5 mm and cutting speed 104 m/ min
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6.2.2 Phase 2: Cutting Temperature Visualization on the Tool
In order to investigate the thermal interaction between the cutting tool with heat source and air as a cooling media, ANSYS CFX software was employed in the current study. ANSYS CFX is CFD software that solves Navier-Stokes equations for the conservation of mass, momentum and energy.
a) Computational Fluid Dynamic (CFD) Model
The CFD model based conjugate heat transfer analysis for the cutting tool was developed in the paper IX. The results were produced by the CFD model using three different levels (0.1, 1 and 10 m/ sec) of the inlet velocity using air as a cutting fluid at 25 °C. The model provided temperature distribution at the cutting tool for each case resulting from the interaction of air at different velocities. Convective heat transfer mode was mainly changed by varying the inlet velocities. Figure 6.7 represents the temperature distribution observed in each case. It can be observed that the higher inlet velocity of 10 m/ sec provided a rapid cooling of the cutting insert by facilitating the convective heat transfer mode.
Figure 6.7a shows that at low inlet air velocity of 0.1 m/sec, temperature drops from 816.8 °C to 485 °C approximately due to the heat transfer of air. Figure 6.7b shows that at low inlet air velocity of 1 m/sec, temperature drops from 816.8 °C to 290 °C approximately. Similarly, in case of 10 m/ sec air velocity temperature dropped from 816.8 °C to 115 °C approximately. This shows that by increasing the air inlet speed higher heat convection is achieved and cutting insert cools down faster. The measurement line passes through the empty hole as well where it is showing the inlet air temperature of 25 °C.
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(a) (b)
(c)
Figure 6:7 Temperature distribution at the cutting tool (a) inlet velocity 0.1 m/sec, (b) inlet velocity 1 m/sec and (c) inlet velocity 10 m/sec
Temperature measurement on the rake face
To enhance the understanding about the convective heat transfer mode resulting from the interaction between the air at 25 °C and uncoated carbide (WC) temperature measurement was first focused at the rake face of the cutting tool. Figures 6.8 show the temperature distribution obtained using air as a cooling media at inlet velocities of 0.1, 1 and 10 m/ sec.
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Figure 6:8 Temperature measurements along the rake face of the cutting tool velocity at 0.1, 1 and 10 m/sec
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Figure 6:9 Temperature distribution at the flank face of the tool at inlet velocity 0.1, 1 and 10 m/sec
Temperature measurement along the flank face
The temperature distribution along the flank face of the cutting tool has been presented in Figure 6.9. Figure 6.9 represent the cooling action of the cutting tool under the influence 0.1, 1 and 10 m/ sec inlet air velocities. It can be observed that higher inlet air velocity results in rapid cooling of the cutting tool due to forced convection.
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6.2.3 FE and CFD based Coupling Procedure
Paper X explains the development of a numerical methodology for the prediction of the convective heat transfer coefficient in dry machining case and furthermore cutting temperature distribution was established utilizing computational fluid dynamics model. The numerical methodology was developed using a coupling procedure between finite element (FE) model and a computational fluid dynamics (CFD) model. The main advantage of this approach is that FE model utilizes material constitutive properties to accurately capture the material behaviour under the machining phase. However the CFD model utilizes the FE based simulated cutting temperature as a heat source and interaction of dry cutting environment was incorporated to predict the temperature distribution on the cutting tool.
Figure 6:10 Concept used for the convective heat transfer using CFD model
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Figure 6:11 FE simulation coupled with CFD model for the optimum value selection of convective heat transfer coefficient
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The numerical methodology also involves a coupling procedure to get the optimum value of convective heat transfer coefficient using an iterative solving approach. Cutting experiments were also performed to verify simulated results. Figure 6.11 provides the flow diagram of the procedure adopted for this study.
To calculate the heat transfer coefficient from the CFD model, basic heat transfer related concepts were utilized. The concepts utilized to perform calculations are mentioned under Figure 6.10. The Figure 6.10 shows a direction (x) used for the calculation of heat transfer involved. At the top of the rake face of the cutting tool, conduction and convection boundary interface has been formed. It means that head conduction at the surface in the x-direction is equals to the heat convection at the surface in the x-direction. The heat transfer calculation for the convection was governed by the Newton’s law of cooling. However, the heat transfer for the conduction was ruled by Fourier’s law of heat conduction.
In order to estimate the precise cutting temperature and reliable temperature distribution on the cutting tool, appropriate incorporation of convective heat transfer in the study is very important. The interaction of cutting tool with the surrounding environment is addressed by the convection heat transfer mode controlled by convective heat transfer coefficient. Previous literature for the machining simulations does not provide much information about the convective heat transfer coefficient selection.
The proposed FE and CFD based approach was applied and first FE simulation was started using a randomly picked value of convective heat transfer coefficient of 100 (W/ m2 K). The FE simulated cutting temperature (avg = 778 C°) form 1st iteration has been shown in Figure 6.12 (c). It can be seen that the difference with experimental temperature is quite high in this iteration.
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(a) (b)
(c) (d)
Figure 6:12 (a) FE machining simulation (b) Experimental cutting temperature (c) Simulated cutting temperature 1st Iteration with h = 100 W/m2K (d) FE Simulated Cutting temperature 2nd Iteration with h = 203.95 W/m2K
After this FE simulation, the temperature of 778 C° was applied as a heat source on the tip of the tool geometry in a CFD model and dry air interaction was developed based on the actual feed rate as shown below in Figure 6.13a. The CFD based calculation shows that convective heat coefficient close to the tool tip is 203.95 W/ m2 K. The calculations for convective heart transfer coefficient (h) values in the CFD model were a performed little away from the tip. To get h value at tool tip, a correction factor has been used by using the trend of (h) at measured points.
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(a) (b)
Figure 6:13 (a) CFD simulation (b) Calculation of convective heat transfer coefficient close to tool tip
The FE simulation was conducted for 2nd iteration using the new convective heat transfer coefficient of 203.95 W/ m2 K. Due to the application of improved convective heat transfer coefficient, new average cutting temperature was reduced to 721. C° from 778 C°. The 2nd CFD iteration was performed using 721 C° as a heat source resulting in the convective heat transfer coefficient of 199.95 W/ m2 K. The Figure 6.14 show the graphical plot that shows that iterative process significantly reduced the percentage temperature error from 22.5% to 15%. In the last two CFD simulations convective heat transfer coefficient appears to be approximately 199.9 W/ m2 K with the relative error of 0.01%, pointing it out as the optimum value under these cutting conditions.
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Figure 6:14 Experimental and simulated cutting temperatures from iterative process
After getting the optimum value of heat transfer coefficient, cutting tool was analysed in CFD model to calculate the temperature mapping on it. Figure 6.15a shows the cutting tool with cutting temperature distribution on it. In Figure 6.15a a line can be seen on the rake face of the cutting tool. This line on the rake face was used later on in Figure 6.15b to represent the temperature on the rake surface. The middle of the insert shows a rapid temperature drop which basically accounts for the empty region present in the middle of the insert.
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(a)
(b)
Figure 6:15 (a) Cutting temperature distribution on the tool (b) Temperature distribution along the line shown in (a)
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The study provided a novel approach based on FE and CFD coupling to utilize the benefits of both techniques. The FE model provides an advantage of utilizing an appropriate material constitutive law, friction law and damage criteria to simulate machining process. On the other hand CFD model simulates the interaction of cutting environment with the cutting tool. The study provided a reasonable agreement between numerical and experimental results.
Reference [1] Pehnt, M. Dynamic lifecycle assessment of renewable energy technologies. Renewable Energy, 2006, 31, 55 –71.
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CONCLUSIONS AND FUTURE WORK
This chapter presents the conclusions drawn from the thesis. In addition to the conclusions, plan for future work is also reported in this chapter.
The work presented in this thesis primarily focused on an investigation of machining performance of aeronautic titanium alloy (Ti6Al4V) by using a vegetable oil based minimum quantity cooling lubrication (MQCL) cooling technique. This was executed by conducting turning experimentation over the recommended range of the cutting parameters. In addition to the experimental work, Finite Element Modeling (FEM) and Computational Fluid Dynamic (CFD) Modeling was also employed to prediction of energy consumed in machining and cutting temperature distribution on the cutting tool. The main conclusions drawn from this research can be summarised as follows:
The beneficial influence of the MQCL (Internal) application method on overall machining performance was more evident at high cutting speeds. The better performance of MQCL (Internal) at higher cutting speed was also found in available literature. A possible reason of this better performance can be attributed t0 the tool-chip contact length in machining. The contact length has a controlling influence on the heat generation at the
“We have the duty of formulating, of summarizing, and of communicating our conclusions, in intelligible form, in recognition of
the right of other free minds to utilize them in making their own decisions”
Sir Ronald Aylmer Fisher (English Statistician)
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secondary deformation zone. The higher cutting speed tends to reduce the contact length, the literature also supports that MQL has a tendency to reduce the contact length. The study provided an insight that increasing the amount of lubricating oil in MQCL does not simply improve the machining performance. The study showed that there is a concern of optimal level in flowrate. The possible explanation of this optimal level can be attributed with the lubrication capacity and associated penetration of the lubricating oil in MQCL arrangement. Another observation was made related to the viscous property of the lubricating oil. Mixing low temperature air with lubricating oil not only acts like a coolant but also increases the viscosity of the oil making it more viscous. It is definitely helpful with respect to the increasing lubrication capacity of the MQCL system, but there is also an issue of penetration with it. More viscous oil is difficult to penetrate in the cutting zone. The better performance of MQCL (100 ml/h) at higher feed rates points out the potential of higher flowrates to be examined for higher feeds as well. Machining performance of MQCL cooling strategies was encouraging as in most cases the tool life was found close to flood strategy or sometimes even better In a study where MQCL system was compared with other cooling strategies, cryogenic machining also turned out to be a recommendable alternative by outperforming flood cooling, even at higher feed and speed. However, the cost to effectiveness extent needs to be further investigated between cryogenic and vegetable oil MQCL system over the wide range of cutting conditions. The FEA modeling-based study proposed a methodology that incorporates finite element machining simulations to predict the energy consumed during the cutting action. However,
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energy consumed in the dry run was measured experimentally to compute the idle energy used by the machine tool. The presented approach was found in good agreement with the experimental findings. The proposed FEA based method was found efficient in calculating the equivalent CO2 emissions involved in the machining operation. In the proposed method, there was no actual machining involved in the computation of the total energy consumed in the machining process. It provides an easier and cheaper solution for energy computation when machining difficult-to-cut materials.
The proposed methodology using Computational Fluid Dynamics (CFD) provided an alternative approach to integrate the influence of cooling media on the cutting tool using a conjugate heat transfer based numerical model. The approach dealt with the presence of a heat source at the tool tip. A solid–fluid interface was then developed using an ANSYS CFX software package. The approach can be further developed to study heat transfer using different types of cutting fluids. The influence of their flow rates can also be predicted. The study provided a novel approach based on FE and CFD coupling to utilize the benefits of both techniques. The FE model provides an advantage of utilizing an appropriate material constitutive law, friction law and damage criteria to simulate machining process. On the other hand CFD model simulates the interaction of cutting environment with the cutting tool. The study provided a reasonable agreement between numerical and experimental results.
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Recommendations for future research work During the current research effort many avenues have been opened which can be explored for future research. They are as follows:
In MQCL (Internal) arrangement different combinations of oil flowrates and air pressures should be examined for higher cutting speeds and feed rates. Mixing the sub-zero temperature air with oil mist has a controlling influence on the viscous properties of the oil. The viscous property of oil has an impact on the lubrication capacity and penetration ability. Different combinations of air and oil mist at different temperatures of air should also be investigated to provide better understanding. This FEA based study has provided details of an initial development of a finite element model for predicting energy in case of turning operation under dry cutting environment. The proposed method can be further extended for other processes like drilling and milling operations for both dry and flood cutting environments. However, finite element simulation of milling and drilling processes is more demanding because of high complexity involved. The FEA based approach can also be used to develop a CO2 emission–based calculator for assessment of environmental impact. In order to develop a CO2 emission calculator for environmental impact, idle energy consumption data of a machine tool should be available at different material removal rates. However, cutting energy can be predicted virtually using finite element simulations. The total energy consumption of a process can be converted into equivalent CO2 emissions based on the selection of geographical location. Advanced CFD multiphase modeling techniques can be employed to model mist based cooling techniques to predict the temperature distributions on the cutting tool under
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different controlled flow rates. The FE and CFD coupled study also provides a way forward for the numerical modeling of hybrid cutting environments like minimum quantity lubrication (MQL) and minimum quantity cooling lubrication (MQCL) strategies. As CFD modeling is capable of providing the numerical solution when it comes to develop the droplets and mixture of two coolants as required in the MQL and MQCL strategies.
The FE and CFD coupled approach provided an optimum value of convective heat transfer coefficient found in reasonable agreement with experimental data. Convective heat transfer coefficient estimation is a difficult task as mentioned in the metal cutting literature. The similar study can also be conducted for different types of coolants and their respective convective heat transfer coefficients can be analyzed. The CFD modeling techniques can be very useful in order to design and visualise the coolant flow when designing customised cutting tools with internal delivery passages. The CFD technique can provide an optimal design solution and can save time and cost required in prototyping of the cutting tools.
