OPTIMIZATION OF PROCESS PARAMETERS IN CNC … · IN CNC PLANE TURNING LATHE USING ... examination the inquiries about are expressed that FR and CS impact the Ra and DOC ... (CNC)

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International Journal of Mechanical Engineering and Technology (IJMET)

Volume 9, Issue 4, April 2018, pp. 396–407, Article ID: IJMET_09_04_045

Available online at http://www.iaeme.com/ijmet/issues.asp?JType=IJMET&VType=9&IType=4

ISSN Print: 0976-6340 and ISSN Online: 0976-6359

© IAEME Publication Scopus Indexed

OPTIMIZATION OF PROCESS PARAMETERS

IN CNC PLANE TURNING LATHE USING

MATLAB

Dr. R.K. Bhuyan

Faculty of Mechanical Engineering, Koneru Lakshmaiah Educational Foundation,

Vaddeswaram, Guntur, Andhra Pradesh, India

B. Rajiv Lekharaju, K.A.N.V. SwathiKiran, M. Naveen Kumar and

K.V.R. Naveen Gupta

UG Scholar, Department of Mechanical Engineering,

Koneru Lakshmaiah Educational Foundation, Vaddeswaram, Guntur, Andhra Pradesh, India

ABSTRACT

In this Present approach a relative report has been finished by utilizing Fuzzy

rationale programming investigation in CNC Turning of Inconel-718 material. The

analysis is trailed by Taguchi L27 under various blend of info parameters, for

example, Cutting Velocity or Cutting Speed (N), Feed Rate (f) and Depth of Cut (D).

Reactions like Material Removal Rate (Mrr), Chip Thickness Ratio (r) and Surface

Roughness (Ra) are considered for monetary change underway and work nature of the

item. The model is created to build up the connection between the information

parameters and the reactions by utilizing the Membership Function (MF). The target

of this model is to discover the anticipated outcomes over an extensive variety of

machining conditions to check the exploratory outcomes. At last, the Analysis of

Variance [ANOVA] procedure is done to check the essentialness of the models and

concentrate the impact of info parameters.

Keywords: CNC turning, Inconel-718, Fuzzy Logic manage, ANOVA, Optimization.

Cite this Article: Dr. R.K. Bhuyan, B. Rajiv Lekharaju, K.A.N.V. SwathiKiran, M.

Naveen Kumar and K.V.R. Naveen Gupta, Optimization of Process Parameters in

CNC Plane Turning Lathe Using MATLAB, International Journal of Mechanical

Engineering and Technology, 9(4), 2018, pp. 396–407.

http://www.iaeme.com/IJMET/issues.asp?JType=IJMET&VType=9&IType=4

1. INTRODUCTION

Turning is one of the primary operations in most of the production process in the manufacture

industry. In the last few year development of importance for CNC machines is day by day

increase and CNC started replacing many conventional machining processes. Recent year the

objective for the research is to achieve the high quality, high standards of accuracy,

Dr. R.K. Bhuyan, B. Rajiv Lekharaju, K.A.N.V. SwathiKiran, M. Naveen Kumar and K.V.R. Naveen

Gupta


dimensional accuracy, and surface finish with high production rate with reduce cost. One of

the material Inconel 718, is a Nickel – Chromium Super-Alloy is a particularly utilized as a

part of an aviation motor, synthetic handling industry, medicinal applications, mostly in gas

turbine segments. This Inconel 718 is developed to them over the top quality and oxidation

protection and link to withstand to raised temperatures in abundance up-to 6500 C. The

machining of this alloy has been found to be a challenging task for the researcher to get the

optimize data for machining.

2. LITERATURE REVIEW:

The current research around there Aruna et.al improve the procedure parameter like cutting

pace, Feed Rate and Depth of Cut to acquire the Surface Roughness when turning of Inconel

718 with cermet embeds. They built up a non-direct relapse condition and express that cutting

velocity has the most grounded impact at first glance harshness among the chose parameters.

Satya Narayan et.al utilized Taguchi based Gray social investigation to enhance the 3 info

parameters to limit the resultant parameters and turning of Inconel 718 material. Ibrahim

et.al utilized the Taguchi Orthogonal Array L27 technique to advance the procedure

parameters with a scope of cutting velocity of 800-1200 m/min, Feed Rate of 0.1– 0.2

mm/rev, and the Depth of Cut is 0.25– 0.6 mm on flank wear, cavity wear, indent wear, and

nose wear of the carbide instrument. They found by SEM comes about that Depth of Cut is a

fundamentally impacts on instrument viability. Barzani et.al talked about the Fuzzy rationale

investigative system to anticipate the machining execution of Al– Si– Cu– Fe pass on

throwing compound. They utilized Pareto-ANOVA to get the best parameter mixes and did

affirmation test analyses and predicated information of Surface Roughness the mistake

discovered just 5.4%. Truong et.al scrutinize the impacts of input and output parameters on

instrument depreciation and Surface Roughness amid plane turning of Inconel Workpiece.

They utilized numerical model created by common logarithm and examination of Variance

procedure to break down the trial result and expressed that cutting velocity impacts more on

wear of hardware and sustain rate is unequivocally affected the surface unpleasantness.

Lahane et.al direct the investigation by Taguchi L27 technique to contemplate the impact of

3 info parameters on material evacuation rate and Surface Roughness (Ra) amid hard turning

the workpiece of Inconel material. From the ANOVA procedure and Taguchi improvement

examination the inquiries about are expressed that FR and CS impact the Ra and DOC impact

the material evacuation rate. Kushwaha et al directed the analysis by Taguchi L27 with three

information parameters to analyses the yield like Mrr and Surface Roughness amid turning of

workpiece of Inconel material under two distinctive machining conditions Dry and Wet. The

Investigators expressed that arrangement of parameter for the best condition for the reactions

are sustain rate, profundity of cut and cutting velocity separately. Xavior et al utilized

Taguchi L27 strategy with Controllable process parameter like Feed, Speed, Depth of cut with

three distinct devices be specific PVD TiAlN carbide, Cubic boron nitride and earthenware to

accomplish the ideal parameters for effective CNC turning of Inconel-718 material. Senthil

Kumar et al enhance the various execution attributes like apparatus wear, Surface Roughness

and Material Removal Rate by utilizing a half breed grey‐fuzzy calculation amid turning of

AISI 1045 steel utilizing uncoated established carbide cutting device. They expressed that

noteworthiness change ideal level process parameter by utilizing this crossover procedure.

Ramanujam et al improve the three information parameters by utilizing consolidate activity

of Gray social coefficient, Principle segment examination and Fuzzy loggy to limit the Ra,

and energy utilization, and boost the material evacuation rate amid turning of Inconel-718

material. Tebassi et al utilizing reaction surface system with Box-plot for creating numerical

models and demonstrating the ordinariness alongside attractive quality way to deal with

Optimization of Process Parameters in CNC Plane Turning Lathe Using MATLAB


enhanced three information parameters on Ra, cutting powers, profitable power devoured

amid turning of Inconel 718 material.

Here our goal is to complete a trial examination on CNC turning of Inconel-718. The

point of this paper is centered around the Fuzzy loggy method and Fuzzy loggy is a potential

instrument to foresee the outcome alongside improve the machining attributes. Likewise,

ANOVA test is done to check the hugeness machining parameter of the created models. At

long last, affirmation test is completed to check the blunder in the model in view of the chose

advancement method.

3. MATERIAL USED:

Inconel 718:

The hardest material which is pertinent in CNC is Inconel 718 Work Piece in which it is a

Nickel Chromium Super based combination. It shows incredibly high return quality and crawl

crack properties at temperatures up to 1300F. It has high weldability.

Material Dimensions: 10mm diameter and 500 mm length.

Material properties:

1. Inconel 718 is a nickel-based super amalgam that is appropriate for applications

requiring high quality in temperature ranges from cryogenic up to 1400°F. Inconel 718

additionally shows fantastic malleable and effect quality.

2. Inconel 718 has great protection from oxidation and erosion at temperatures in the

amalgam's valuable quality range in environments experienced in stream motors and

gas turbine activities.

3. High thermal conductivity and thermal strength.

Design of Experiment:

The experiments are conducted by Taguchi L27 and the selected process parameters such as

CS, FR and DOC and every parameter have 3 stages from the related research review the

procedure factors with their genuine esteems on various levels are appeared in the Table 1.

Experimental setup and work:

The work pieces materials used for the experiment are Inconel 718. The dimension of the

solid round bar as the work pieces material for conducting the turning operation are 9 mm in

diameter × 100 mm in long. The operations are conducted on Computer Numerically

Controlled (CNC) Machine Emco PC Turn 345 with the maximum spindle speed is 3000

rev/min with Cemented carbide tool nose radius4mm as shown in Fig.


Gupta


Figure 1 Experimental set up

The accompanying connection is utilized to assess the Material Removal Rate (Mrr), Chip

Thickness Ratio (r) and Surface Roughness (Ra) as obsessive beneath.

Material Removal Rate (Mrr): Material evacuation rate is exhibited as the proportion of

contrasts in Final Substantial weight before machining (Wb) and after machining (Wa) to the

machining time (t).

Scientifically it is communicated as

MRR

mg min

where,

Wb = Final Substantial weight before machining (mg).

Wa = Final Substantial weight subsequent to machining (mg).

Chip Thickness Ratio (r): The chip thickness proportion is estimated by the three normal

estimations of the thickness subsequent to machining. The chip thickness estimated by

advanced Vernier caliper.

Surface Roughness (Ra): The Surface Roughness Ra esteems are estimated by the

Instrument or Machine called "MITUTOYO" Surface harshness analyzer at three one of a

kind positions in the machining surface of the work piece and typical of the three estimations

are tabulated.

4. METHODOLOGY:

Fuzzy logic Modelling for CNC machining:

Fuzzy Loggy programming is a Multi-Reasoning coherent idea in view of human conduct

where the assessment in light. Fuzzy Loggy demonstrating programming is as far as human

etymological factors are characterized as far as to a great degree little, little, little, medium,

less high, high, high and amazingly high and so forth. The Fuzzy Loggy demonstrating

framework have two kind of framework Mamdani and Sugeno, The Mamdani framework

predominantly comprises of information definition, Implication, Aggregation and

Defuzzification. As far as information definition every one of the sources of info and yields

information are accepted to choose a model. Next in the collections procedure stirred up the

all principles yields. At long last, Defuzzification process changes over the total yield to

Single Number.

In this examination 3 input process parameters are viewed as like CS, FR and DOC and

three yield reactions likely Material evacuation rate (Mrr), chip thickness proportion (r) and

Surface unpleasantness (Ra) are chosen for this Fuzzy Loggy demonstrate. The Logic is done

by Fuzzy Loggy Compound System [fis] as appeared in Figure1.



Result and Discussion:

The given and final parameters are shown as semantic factors or Fuzzy Loggy participation

work. All the info and yield parameters are in Triangular enrolment work. For each input

DOC, FR and CS enrolment work likely least, medium and most extreme as appeared in

Figure 2. Similarly, for yield five factors such little, little, medium, expansive, extensive are

allocated to MRR, r and Ra as appeared in Figure.

Fuzzy interface system

After generalized the membership function the Fuzzy loggy rules were applied by the given

and the final outputs in the form of “If - Then Control” rule. The rules are executed by

MATLAB R14 with Fuzzy Loggy toolbox environment having 27 tests rules with

theirlinguistic Participated Functions are executed well in Figure 8.

Group of standards relies upon the Participated Function numbers in every information

factors to obtain anticipated outcome. An Anticipated outcome is as far as contribution for the

Proper Coding to get the correct an incentive which has appeared at Figure 9.

rror redicted result xperimental result

redicted result

This test analyses the Error Percentage in the trial esteem and the anticipated esteem

Fuzzy Loggy run the show. The level of mistake is ascertained according to the Equation 1 as

appeared in Table 6, Table 7 and Table 8 for computing MRR, r and Ra individually. It

achieves that the normal level of blunder for MRR, r and Ra are begun by 19.48%, 15.77%

and 14.61% individually.


Gupta


Table information:

Table 1 Info Parameters and their stages

Parameters Symbols Units Stages

-1 0 1

Cutting

Speed N m/min 800 1000 1200

Depth of Cut D mm 0.25 0.4 0.6

Feed Rate f mm/rev 0.1 0.15 0.2

Table 2 Systematic Tabulated Results

SNO N f D MRR

(mg/min) r

Ra

(Micron)

1 1 1 1 2.33 0.733 0.8800

2 1 1 2 2.34 0.734 0.9200

3 1 1 3 2.35 0.833 0.6500

4 1 2 1 2.45 0.882 1.4100

5 1 2 2 2.48 0.891 1.2000

6 1 2 3 2.49 0.890 1.1000

7 1 3 1 2.46 0.705 1.9610

8 1 3 2 2.48 0.683 1.3500

9 1 3 3 2.49 0.744 1.1900

10 2 1 1 2.58 0.615 0.7650

11 2 1 2 2.59 0.552 0.8020

12 2 1 3 2.57 0.653 0.6200

13 2 2 1 2.89 0.750 1.1000

14 2 2 2 2.91 0.751 0.9040

15 2 2 3 2.90 0.752 0.6200

16 2 3 1 2.87 0.769 1.7600

17 2 3 2 2.88 0.771 1.1200

18 2 3 3 2.91 0.771 0.8950

19 3 1 1 2.56 0.526 0.7370

20 3 1 2 2.55 0.523 0.8150

21 3 1 3 2.54 0.521 0.6230

22 3 2 1 2.74 0.714 1.1000

23 3 2 2 2.72 0.611 0.9175

24 3 2 3 2.73 0.702 0.8100

25 3 3 1 2.83 0.695 1.2100

26 3 3 2 2.84 0.661 1.3350

27 3 3 3 2.83 0.654 0.6420



Table 3 Comparison of MRR with the Systematic Tabulated Results and FUZZYLOGGY Forecasted

Results

SNO MRR

Experimental

MRR

Forecasted

Defect

(%)

1 2.381 2.386 0.210

2 2.623 2.631 0.304

3 2.355 2.414 2.444

4 2.452 2.512 2.389

5 2.487 2.547 2.356

6 2.491 2.565 2.885

7 2.463 2.507 1.755

8 2.485 2.495 0.401

9 2.492 2.516 0.954

10 2.581 2.602 0.807

11 2.597 2.62 0.878

12 2.578 2.602 0.922

13 2.892 2.882 -0.347

14 2.913 2.813 -3.555

15 2.905 2.82 -3.014

16 2.871 2.721 -5.513

17 2.883 2.892 0.311

18 2.912 2.921 0.308

19 2.563 2.581 0.697

20 2.554 2.591 1.428

21 2.541 2.611 2.681

22 2.743 2.712 -1.143

23 2.721 2.698 -0.852

24 2.736 2.731 -0.183

25 2.831 2.833 0.071

26 2.846 2.831 -0.530

27 2.833 2.836 0.106

Table 4 Comparison of r with the Systematic Results and FUZZYLOGGY Forecasted Results

SNO r

Experimental

r

Forecasted

Defect

(%)

1 0.733 0.7005 -4.640

2 0.734 0.7516 2.342

3 0.833 0.8577 2.880

4 0.882 0.8586 -2.725

5 0.891 0.8679 -2.662

6 0.89 0.8655 -2.831

7 0.705 0.7155 1.468

8 0.683 0.6633 -2.970

9 0.744 0.7677 3.087

10 0.615 0.6233 1.332


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11 0.552 0.5855 5.722

12 0.653 0.6674 2.158

13 0.75 0.7677 2.306

14 0.751 0.7455 -0.738

15 0.752 0.7255 -3.653

16 0.769 0.7455 -3.152

17 0.771 0.7516 -2.581

18 0.771 0.7855 1.846

19 0.526 0.5405 2.683

20 0.523 0.5205 -0.480

21 0.521 0.53055 1.800

22 0.714 0.7155 0.210

23 0.611 0.62155 1.697

24 0.702 0.7055 0.496

25 0.695 0.7055 1.488

26 0.661 0.6755 2.147

27 0.654 0.6633 1.402

Table 5 Comparison of Ra with the Systematic Results and FUZZYLOGGY Forecasted Results

SNO Ra

Experimental

Ra

Forecasted

Defect

(%)

1 0.88 0.8905 1.179

2 0.92 0.9516 3.321

3 0.65 0.6977 6.837

4 1.41 1.48586 5.105

5 1.2 1.08679 10.417

6 1.1 0.9655 -13.931

7 1.961 1.67055 -5.414

8 1.35 1.36633 1.195

9 1.19 1.21977 2.441

10 0.765 0.7233 -5.765

11 0.802 0.81855 2.022

12 0.62 0.6274 1.179

13 1.1 0.9977 -10.254

14 0.904 0.8455 -6.919

15 0.62 0.6355 2.439

16 1.76 1.67655 -4.977

17 1.12 1.07516 -4.171

18 0.895 0.9855 9.183

19 0.737 0.7505 1.799

20 0.815 0.8505 4.174

21 0.623 0.63055 1.197

22 1.1 1.17155 6.107

23 0.9175 1.062155 13.619

24 0.81 0.8355 3.052



25 1.21 1.2155 0.452

26 1.335 1.3655 2.234

27 0.642 0.6533 1.730

ANALYSIS OF VARIANCE (ANOVA)

Moreover, the ANOVA test is sorted out to confirm in which of the parameters essentially

influence the execution qualities. Here are the last aftereffects of the ANOVA for yield

parameters MRR, r, Ra, Table7 to 9 individually. P Value should be under 0.05, then model is

remarkable, and 95% certainty level exists. Here R2 and ADJ R2 is to demonstrate the

decency to fit for the model the esteem near solidarity show the exploratory info.

Here r2 and ADJ r2 for MRR is 97.12%, 95.60%, for r 91.21 %, 86.56% and for Ra

90.48%, 85.43% separately.

Interpretation of Plots: The information is additionally examined for concentrate on sum

machining parameters with the reaction utilizing the principal impact plots with the assistance

of programming bundle MINITAB16. These plots demonstrate the variety in the individual

reaction with the 3 parameters, i.e., CS, FR and DOC independently. Here the Impact plots

are utilized to decide the ideal outline necessities to get last ideal estimation of reactions. The

Fig. 2 demonstrates the primary impact plot for Mrr. The outcomes demonstrate that with the

expansion in FR and DOC there is a ceaseless addition to Mrr and lessening in unpleasantness

with expanded cutting velocity. Fig. 3 demonstrates the fundamental impact plot for chip

thickness proportion (r) It appears with increment in speed the 'r' esteem diminishes however

on the off chance that bolster rate the 'r' the esteem builds first at that point diminish step by

step. Because of Depth of cut the estimation of 'r' is most extreme at starting and last level of

process parameter. Fig 4: The Ra esteem result demonstrates that with the bring up in

encourage there is a nonstop bring up in surface harshness esteem. Here the fundamental

impact plot demonstrates that the abatement in Surface Roughness with expanded Cutting

Speed.

321

1.2

1.1

1.0

0.9

0.8

321

321

1.2

1.1

1.0

0.9

0.8

N

Me

an

F

D

Main Effects Plot for RaData Means

321

2.8

2.7

2.6

2.5

2.4

321

321

2.8

2.7

2.6

2.5

2.4

N

Me

an

F

D

Main Effects Plot for MRRData Means

Table 6 Analysis of Variance for „MRR‟

Source DF Seq SS ADJ MS F p

D 1 0.338939 0.338939 201.47 0.000

F 1 0.264022 0.264022 156.94 0.000

V 1 0.000556 0.000556 0.33 0.573

D*D 1 0.294817 0.294817 175.24 0.000

F*F 1 0.048600 0.048600 28.89 0.000

V*V 1 0.000067 0.000067 0.04 0.845

D*F 1 0.016133 0.016133 3.54 0.037


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D*V 1 0.001200 0.001200 9.59 0.007

F*V 1 0.000533 0.000533 0.71 0.410

Residual

Defect 17 0.028600 0.001682

Total 26 0.993467

321

0.80

0.75

0.70

0.65

0.60

321

321

0.80

0.75

0.70

0.65

0.60

N

Me

an

F

D

Main Effects Plot for rData Means

Table 7 Analysis of Variance for „r‟


D 1 0.33935 0.02005 20.02 0.000

F 1 1.20177 0.05217 70.91 0.000

V 1 0.79086 0.00004 46.67 0.000

D*D 1 0.05217 0.11488 3.08 0.097

F*F 1 0.00004 0.08996 0.00 0.961

V*V 1 0.00792 0.00958 0.47 0.503

D*F 1 0.08996 0.08996 5.31 0.034

D*V 1 0.00958 0.00958 0.57 0.462

F*V 1 0.24510 0.24510 14.46 0.001

Residual Defect 17 0.28810 0.01695

Total 26 3.02485

Table 8 Analysis of Variance for „Ra‟


D 1 0.118098 0.118098 81.71 0.000

F 1 0.020200 0.020200 13.98 0.002

V 1 0.000953 0.000953 0.66 0.428

D*D 1 0.000929 0.000929 0.64 0.434

F*F 1 0.062901 0.062901 43.52 0.000

V*V 1 0.016189 0.016189 11.20 0.004

D*F 1 0.030805 0.030805 21.31 0.000

D*V 1 0.003502 0.003502 2.42 0.138

F*V 1 0.001474 0.001474 1.02 0.327

Residual Defect 17 0.024571 0.024571

Total 26 0.279623



To evaluate the optimize process parameter the experiment is again analyzed based on

their mean value result at different level at shown in Table 7. From the table the bold values

obtained indicates the level of noteworthy outfit parameters for which the best outcomes are

accomplished and after that the ideal outline is figured.

Table 9 Mean Values of MRR, r, Ra at various stages

Stage MRR r Ra

N f D N f D N f D

1 2.470 2.530 2.642 0.788 0.706 0.632 1.162 0.756 1.191

2 2.792 2.704 2.679 0.709 0.686 0.771 0.954 1.017 1.040

3 2.708 2.735 2.649 0.623 0.724 0.717 0.909 1.251 0.794

Delta 0.084 0.205 0.037 0.165 0.038 0.139 0.253 0.495 0.397

Rank 1 2 3 1 3 2 2 3 1

It is observed that for MRR, r and Ra the optimum levels are 2,3,2: 1,3,2 and 1,3,1 for N, f

and D respectively. It is analyzed that for MRR the experiment 17, for r the experiment no 8

and for the Ra the experiment no 7. the experimental result and the predicated result with their

error shown in Table 8.

Table 10 Predicted experiment values

Machining

characteristics Parameters setting

Experimental

values

Predicted

values

% of

Error

MRR

V=40

F=0.2

D=0.4

2.883 2.892 0.311

r

N=30

F=0.2

D=0.4

0.683 0.6633 -2.970

Ra

N=40

F=0.2

D=0.2

1.961 1.67055 -5.414

GRAPHICAL INFORMATION:


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5. CONCLUSION:

In view of the above hypothesis, the accompanying conclusions are as per the following:

The show day examination is to think about the machining yield reactions of 'MRR', 'r'

and 'Ra' of Inconel-718 amid CNC turning operation.

The Suggested Fuzzy Loggy gives an exact and simple most assurance for anticipated

effects in the yield achieved parameters.

An association between the decided Fuzzy results and Experimental results is found

after the relationship between foreseen regards and the Experimental regards which

are considered.

It likewise underscores the approvals of the Fuzzy Loggy comes about with the

exploratory outcome that gives 95% of precision esteem.

Also, ANOVA. Test is led to get connotation of each procedure parameter with the

chose reaction.

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[2] Suresh P. V. S, Rao P. V and Deshmukh S. G, A hereditary algorithmic approach for

enhancement of surface unpleasantness expectation demonstrate, International Journal of

Machine Tools and Manufacture, Vol.42, 2002, pp. 675– 680.

[3] Saravanan. R Asokan. P, and Sachidanandam. M, A multi-objective hereditary calculation

(GA) approach for streamlining of surface pounding activities, International Journal of

Machine Tools and Manufacture, Vol.42,2002, pp.1327– 1334.

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Documents

OPTIMIZATION OF PROCESS PARAMETERS IN CNC … · IN CNC PLANE TURNING LATHE USING ... examination the inquiries about are expressed that FR and CS impact the Ra and DOC ... (CNC)