[IJET V2I4P3] Authors: N.Keerthi, N.Deepthi,N.Jaya Krishna

International Journal of Engineering and Techniques - Volume 2 Issue 4, July – Aug 2016

ISSN: 2395-1303 http://www.ijetjournal.org Page 17

Modeling and Analysis of Machining Characteristics of Metal Matrix Composite in Milling Process

N.Keerthi1, N.Deepthi2,N.Jaya Krishna3 1, 2, 3Mechanical Engineering, Annamacharya Institute of Technology and sciences Autonomous and Rajampet

I.INTRODUCTION In the area of globalization

manufacturers are facing the challenges of higher Quality and productivity are two important . Productivity can be interpreted in terms of material removal rate in the machining operation and quality represents satisfactory yield in terms of product characteristics as desired by the customers. but conflicting criteria in any machining operations. In order to ensure high productivity, extent of quality is to be compromised. It is, therefore, essential to optimize quality and productivity simultaneously. Dimensional accuracy, form stability, surface smoothness, fulfillment of functional requirements in prescribed area of application etc. are important quality attributes of the product. Increase in productivity results in reduction in machining time which may result in quality loss. On the contrary, an improvement in quality results in increasing machining time thereby, reducing productivity. Therefore, there is a need to optimize quality as well as productivity. Optimizing a single response may yield positively in some aspects but it may affect adversely in other aspects. The problem can be overcome if multiple objectives are optimized simultaneously. It is, therefore, required to maximize material removal rate (MRR), and to improve product quality simultaneously by selecting an appropriate (optimal) process environment. To this end, the present work deals with multi-objective optimization philosophy based on Taguchi-Grey

relational analysis method applied in CNC end milling operation. II. STIR CASTING PROCESS: In a stir casting process, the reinforcing phases are distributed into molten matrix by mechanical stirring. Stir casting of metal matrix composites was initiated in 1968, hen S. Ray introduced alumina particles into aluminum melt by stirring molten aluminum alloys containing the ceramic powders. Mechanical stirring in the furnace is a key element of this process. The resultant molten alloy, with ceramic particles, can then be used for die casting, permanent mold casting, or sand casting. Stir casting is suitable for manufacturing composites with up to 30% volume fractions of reinforcement. The cast composites are sometimes further extruded to reduce porosity, refine the microstructure, and homogenize the distribution of the reinforcement. A major concern associated with the stir casting process is the segregation of reinforcing particles which is caused by the surfacing or settling of the reinforcement particles during the melting and casting processes.The final distribution of the particles in the solid depends on material properties and process parameters such as the wetting condition of the particles with the melt, strength of mixing, relative density, and rate of solidification.The distribution of the particles in the molten matrix depends on the geometry of the mechanical stirrer, stirring parameters, placement of the mechanical stirrer in the melt, melting temperature, and the characteristics of the particles added.

RESEARCH ARTICLE OPEN ACCESS



III. COMPOSITE MATERIAL PREPARATION: For composite material selection of Matrix and reinforcement are of prime importance. For this research work we had selected material as follows. Matrix Aluminium alloy 2000, 6000 and 7000 series are used for fabrication of the automotive parts. PAMC under study consist of matrix material of aluminium alloy Al6082 whose chemical composition is shown in the Table. An advantage of using aluminium as matrix material is casting technology is well established, and most important it is light weight material. Aluminium alloy is associated with some disadvantages such as bonding is more challenging than steel, low strength than steel and price is 200% of that of steel. But with proper reinforcement and treatment the strength can be increased to required level. Reinforcement Particles of Al2O3, magnesium and zinc are used as reinforcement.

Table 1.Specifications Of Cnc Milling Machine

Fig 1.Expermential set up ( CNC Machnie)

IV. WORK MATERIALPREPARATION The work material is cut as required sizes of 90x90x12 mm from Al6082-Mg-Zn alloy matrix raw stock to perform milling operation on them. These work materials are prepared by using the stir casting process.

Technical specifications Travels X axis 225 mm Y axis 150 mm Z axis 115 mm Distance between Table top and spindle nose

70-185 mm

Table size 360mm*132 mm Spindle Spindle motor capacity 0.4 kw Programmable spindle speed 150-3000rpm Spindle nose taper BT 30 Accuracy Positioning 0.010 mm Repeatability +_0.005 mm Feed Rate Programmable feed rate X Y Z axis

0-1.2 mm/min

CNC controller Control system PC based 3 Axis

continuous path Power source 230V, single phase, 50 Hz

International Journal of Engineering and Techniques

ISSN: 2395-1303

Fig 3 Strining of metals

Fig 4 Melting of alloys

Fig .5Pouring of molten metal into mouldThe required work materials are prepared by using the stir casting process with three different compositions of aluminumzinc alloy matrix.

Fig 6 Talysurf meter

V. EXPERIMENTAL PROCEDURE The Input parameters of the milling

process and their levels (each input parameter has three levels) are listed based on previous works (Table 1.2).

International Journal of Engineering and Techniques - Volume 2 Issue 4, July

1303 http://www.ijetjournal.org

Melting of alloys

Pouring of molten metal into mould

The required work materials are prepared by using the stir casting process with three different compositions of aluminum-copper-

EXPERIMENTAL PROCEDURE

The Input parameters of the milling process and their levels (each input parameter has three levels) are listed based on previous works (Table 1.2).

Milling operation is performed on Al 6082-Cu-Zn alloy work material according to full factorial design using CNC milling machine. The surface roughness values are measured using Talysurf meter . The Metal removal rate is calculated by means of formula is given by

Table 2. Process parameters and their levels

Symbol Machining parameter Unit

A Spindle speed rpm B Feed Mm/minC Depth of cut mm

VI. Results from ANN Table 3. Experimental data

Speed Feed Depth of cut 1800 75 0.75 1400 75 0.51400 100 0.751600 75 1 1600 100 0.51400 50 0.51400 50 0.751400 75 1 1600 75 0.5

July – Aug 2016

Page 19

Milling operation is performed on Al Zn alloy work material

ull factorial design using CNC milling machine. The surface roughness values are measured using Talysurf meter . The Metal removal rate is calculated by means of formula is given by

Process parameters and their

Level1 Level2 Level3 1400 1600 1800

Mm/min 50 75 100 0.5 0.75 1

Experimental data Depth of cut MRR Ra

0.75 557.413 2.494 0.5 369.003 2.325

0.75 744.909 1.469 757.95 2.774

0.5 502.26 1.399 0.5 249.79 0.866

0.75 377.99 2.46445 738.91 4.1435

0.5 376.175 1.0125



Table 4. Comparison between Experimental and values

VI.RESULTS FROM TAGUCHI: From the graph the results predicted are Graph for MRR

1600 75 0.5 376.175 1.0125 1800 50 0.75 373.567 0.912 1400 100 1 1013.34 2.304 1600 50 0.75 375 .8245 1600 50 1 499.583 1.88 1400 50 1 499 1.85 1800 50 0.5 246.79 0.9055 1800 100 0.75 749.375 1.405 1600 50 0.5 248.18 0.9975 1400 100 0.5 503.94 1.435 1800 75 1 750.469 2.858 1600 100 0.75 751.252 1.169 1800 50 1 508.345 2.6935 1800 100 1 998.17 1.441 1800 75 0.5 370.461 1.3735 1800 100 0.5 492.935 1.3645 1600 75 0.75 562.5 1.368 1600 100 1 1021.27 1.6585 1400 75 0.75 565.82 2.5195

Actual MRR

Predicted MRR

Actual Ra

Predicted Ra

557.413 510.65 2.494 2.152 369.003 323.63 2.325 2.048 744.909 706.395 1.469 1.568 757.95 710.652 2.774 2.568 502.26 461.857 1.399 1.5144

249.79 220.36 0.866 1.095 377.99 312.265 2.46445 2.124 738.91 685.32 2.1435 2.895

376.175 325.822 1.0125 1.231 373.567 315.236 0.912 1.125 1013.34 995.495 2.304 2.0135

375 304.23 1.8245 1.645 499.583 436.87 1.88 1.624 499.375 425.963 2.888 2.235 246.79 213.262 0.9055 1.236

749.375 702.965 1.405 1.0312 248.18 224.586 0.9975 1.321 503.94 449.56 1.435 1.125

750.469 706.95 2.858 2.452 751.252 680.569 1.169 1.523 508.345 487.95 2.6935 2.158 998.17 945.562 1.441 1.875

370.461 335.26 1.3735 1.468 492.935 482.62 1.3645 1.568

562.5 521.354 1.368 1.647 1021.27 978065 1.6585 1.425 565.82 524.52 2.5195 2.145



Optimum input parameters are Speed;1400rpm Feed:100mm/min Doc:1mm Graph for Ra

The optimum set of input parameters are: Speed;1400rpm Feed: 50mm/min Doc:0.5mm RESULTS FROM ANOVA: Anova method is used to find the effect of input parameters on output parameters. The effect is individually find out are Table 5.Anova For MRR

Source DF SS MS VARIENCE

St.Dev % TOTAL

Speed 2 125.3480 62.6740

1882.367 10.253 2.10

Feed 6 642023.8445

107003.9741

22652.220

150.507 35.71

Doc 18 702851.6382

39047.3132

39047.313

197.604 62.29

From the table it is found that

The MRR is mostly influenced by DOC about 62.29 % of MRR is influenced by DOC This is because by increasing the DOC the volume of material removed is increased.

Table 6. ANOVA For surface roughness:

Source DF SS MS VARIENCE

St.ev % TOTAL

Speed 2 2.4675

1.338 0.064 0.253 9.54

Feed 6 3.9546

0.6591 0.07 0.163 3.99

Doc 18 10.4205

0.5789 0.579 0.761 86.47

Ra is mostly effected by Depth of cut

.it is almost effected by 87% We already know that surface roughness is more if we remove more amount of material in single cut.

VII. CONCLUSIONS In the present work an Artificial Neural Network (ANN) model has been developed to predict the response (output) parameters surface roughness, and material removal rate in Milling process. The controllable parameters such as cutting speeds, feed rate and depth of cut which influence the responses are identified and analyzed. The optimum combinations of (input) process parameters are determined by Taguchi method. For producing low value of surface roughness, the optimum parameter values are spindle speed (V) 1400, feed (f) 50, Depth of cut (t)0.5.

321

-2-4-6-8

321

321

-2-4-6-8

A

Mean

of SN

ratio

s

B

C

Main Effects Plot for SN ratiosData Means

Signal-to-noise: Smaller is better



For high value of material removal rate, the optimum parameter values are spindle speed (V) 1400, feed (f) 1, depth of cut (t) 1. The analysis of variance (ANOVA) is also employed to find the contribution of input parameters on output parameters. Surface roughness is mostly affected by Depth of cut. Material removal rate is mostly affected by Depth of cut.

VIII. FUTURE SCOPE Similar type of techniques is used

for engineering materials like different processes. The Artificial Intelligence Fuzzy logic can also be used for prediction of machining responses. ANFIS can also be used for prediction of machining responses.

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