MULTI VARIATE OPTIMIZATION OF THE INFLUENCE OF EDGE GEOMETRY IN ORTHOGONAL MACHINING OF Ti6Al4V USIN

International Journal of Mechanical and Production Engineering Research and Development (IJMPERD) ISSN (P): 2249–6890; ISSN (E): 2249–8001 Vol. 10, Issue 3, Jun 2020, 9–20 © TJPRC Pvt. Ltd.

MULTI VARIATE OPTIMIZATION OF THE INFLUENCE OF EDGE GEOMETRY IN ORTHOGONAL MACHINING OF Ti6Al4V USING PCBN INSERTS BY TAGUCHI AND GREY RELATIONAL ANALYSIS V. VEERANAATH Assistant Professor, Department of Mechanical Engineering, Faculty of Engineering and Technology, SRM Institute of Science and Technology, Kattankulathur, India ABSTRACT High hardened and resistant metals are of great requirement in aeronautical industries but metal cutting of these said metals are unfeasible and also not cost efficient. Hence, for machining the above said materials, special purpose machining and inserts are required. In addition, during metal cutting the insert damage is chiefly triggered by the development of a sticking together layer and built-Up Edge (BUE) necessitating a lessening of the insert life. The creation of sticking together layer on the insert lessens the effectiveness and efficiency of machining insert and degrades surface finish and measurement accurateness of the ended job. The cutting nose radius of the insert straight impact the

in the similar direction of scale. In this paper, orthogonal cutting tests on Ti6Al4V were performed to learn the influence of edge geometry on the machinability using PCBN tool inserts. Machining forces, temperature, surface roughness and tool wear were studied and analysed for comparison before and after insert tip modification. Nose radius of the insert in these experiments are concocted and altered by means of wire EDM machine. Also, a range of experiments by varying machining peed and feed rate were conducted for comparative study. Grey Relation analysis is employed to find the optimum values.

Original Article

chip development contrivance in orthogonal machining where the insert tip radius and the machined chip thickness are

KEYWORDS: Orthogonal Machining, Ti6Al4V, Edge Geometry, Grey Relation Analysis & ANNOVA

Received: Mar 02, 2020; Accepted: Mar 22, 2020; Published: Apr 21, 2020; Paper Id.: IJMPERDJUN20202

INTRODUCTION Titanium alloys like Ti6Al4V play a major role in aeronautical, vehicle, oil and many industries because of its remarkable properties like improved strength and other mechanical properties [1]. But the only disadvantage with these alloys is they are very hard to metal removal because of high stress, strain, temperature and machining forces [2]. This can be comparatively reduced to a greater extent by uses of coolants but they are not eco viable which induces to avoid the usage of it. This gives a pathway for the use of special type of inserts to machine these alloys.

The commonly observed issue in the usage of these inserts is the development of sticky metal edge which degrades the quality of tool by increasing tool wear and reducing tool life [3]. These kinds of sticky layers of material formed by the deposition of work material can be comparatively reduced by modifying the tool nose dimension. The dimensions of edge of the insert may affect the surface veracity also [4]. Therefore, more research is oriented to modification of nose radius of the tool towards the attained better output responses [5].

Fatih Hayati Cakir [6] developed a finite element analysis model to understand the turning process on this Titanium alloy. He also studied the variation of tool wear and exterior quality based on the various output

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parameters. Siddharam Mane [7] studied the effect of cooling result on the metal cutting of titanium super alloy and studied the possessions of the same on tool wear and surface quality. He also developed a finite element model to study tool and workpiece temperature and correlate the same. Fei Shuang [8] developed a numerical model to study about the various governing parameters on chip thickness and temperature. Chenwei Shan [9] established a forecast prototypical to forecast and study the various output responses with available experimental data as input. Sarvesh Kumar Mishra [10] developed a unified maintainable method for cutting of Ti6Al4V with textured tools. The coolant and insert founded practises are combined to educate the outcomes of cutting like forces, inertia, contact parameters, etc by studying the influencing mechanism. Adam D. Lampropoulos [11] elaborated the numerical imitations of metal removal of Ti6Al4V titanium with two numerical techniques on standardisation and the renormalization and this parametric examination is supplemented by showing numerical imitations with opposite.

By considering the above said facts, this paper is mainly focussed on the experimental investigations of the effect of edge geometry on the metal cutting of super alloy Ti6Al4V. The various output responses were studied before and after the modification of the insert. The various metal cutting parameters were also modified based on L9 Taguchi array to educate the consequence of the same and the analysis were also done to obtain the optimum values of cutting constraints.

METHODS Metal cutting of the work part Ti6Al4V is carried on CNC machining centre in the form orthogonal turning process. The initial dimensions of the job are 250mm length and 30mm diameter. PCBN inserts were used to remove the material in the means of chip during machining process. The insert dimensions were used in Wire EDM machine by using appropriate wire for metal cutting. By modifying so, the edge radius of the insert is modified from 0.8 to 0.4mm. The output responses of the experiment were recorded and compared before and after the modification of the insert edge radius. The cutting constraints like machining speed, feed and depth of cut were varied. These constraints were varied according to L9 Taguchi array of DOE. The constraints were varied in three levels for each of three factors. The machining speed were varied as 60, 80 and 100m/min in three levels, feed is varied as 0.1, 0.15 and 0.2mm/rev and finally the depth of cut as 1, 1.5 and 2mm respectively. The machining forces which are recorded along the cutting direction in the interface region are measured using Piezo electric Kiztler dynamometer. The dynamometer is properly calibrated and set to record the force along the machining direction. The temperature during metal removal at the contact of the insert and the workpiece were recorded using Non-Contact IR temperature sensor. The heat which is generated out from machining interfacing region is recorded without the usage of coolants. The surface roughness after machining in the workpiece is found using roughness tester and tool wear is measured using image analyser attached with optical microscope. Taguchi and Grey Relation analysis were used to find the optimal values.

RESULTS Table 1 displays the experimental outcomes of the output responses observed while machining the super alloy with insert of nose radius of 0.8mm.

Table 1: Experimental Results while Machining with Insert of Nose Radius 0.8mm Exp. No

Cutting Speed (m/min)

Feed (mm/rev)

Depth of Cut (mm)

Cutting Force (N)

Temperature (oC)

Surface Roughness (µm)

Tool Wear (µm)

1 2

60 60

0.1 0.15

0.5 1

526 534

382 561

0.6475 1.1654

310.66 471.64

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3 4 5 6 7 8 9

60 80 80 80 100 100 100

0.2 0.1 0.15 0.2 0.1 0.15 0.2

1.5 1 1.5 0.5 1.5 0.5 1

558 561 576 583 601 634 667

534 665 464 526 434 452 563

11

1.2823 0.2686 0.4624 1.0582 0.6989 0.4788 0.8821

466.44 631.14 376.43 465.32 362.49 408.94 535.96

From the table 1, it is Clear that Minimum Cutting Force and Temperature of 526N and 382oC observed at experimental condition of 60m/min, 0.1mm/rev and 0.5mm because the values are comparatively when the material removal is less in this. But, better surface finish is obtained only at 80m/min, 0.1mm/rev and 1mm wherein only a nominal speed with a better exterior quality is obtained. But to the contrary, at the same experimental conditions even though better finish is obtained, tool wear is more because of the deformation. Table 2 displays the trial outcomes of the output responses observed while machining the super alloy with insert of nose radius of 0.4mm.

Exp. No 1 2 3 4 5 6 7 8 9

Table 2: Experimental Results while Machining with Insert of Nose Radius 0.4mm Cutting Speed Feed Depth of Cut Cutting Temperature Surface (m/min) (mm/rev) (mm) Force (N) (oC) Roughness (µm) 60 0.1 0.5 432 237 0.1273 60 0.15 1 475 407 0.635 60 0.2 1.5 492 392 1.045 80 0.1 1 521 506 0.4086 80 0.15 1.5 536 521 0.733 80 0.2 0.5 576 435 0.6576 100 0.1 1.5 564 485 0.4832 100 0.15 0.5 573 653 0.6448 100 0.2 1 581 432 0.6954

Tool Wear (µm) 236.03 342.54 375.94 420.54 441.11 431.71 420.3 602.05 415.77

As similar to the case of machining with tool insert of nose radius of 0.4mm, it is evident that cutting force and temperature is found to be minimum at same experiment conditions of low cutting speed where the MRR is minimum. But again at nominal speed of 80m/min only as like the previous case of better surface finish. But unlike the insert before modification, insert is worn out more at a higher speed of 100m/min because of thermal deformation. Figure 1 shows the variation of output responses like machining forces, temperature, tool wear and surface roughness with respect to various process parameters. From figure 1, it is vivid that before and after modification of insert the cutting force rises with the rise in cutting speed and rise in feed but with respect to depth of cut, it increases only up to 1mm and then it reduces in case of 0.8mm insert, but it is vice versa at the point when nose radius is 0.4mm. Aswhen the machining load and the speed with which tool iare fed against the job is at a greater condition, the force also increases. But there is no similarity in the trend followed in the temperature observed at the contact region. It is observed that temperature is found to upsurge up to medium level of depth of cut and machining speed constraint, and then it found to fall in case of 0.8mm insert, but the output response is found to rise with rise of the same constraints in case of 0.4mm because of the insert and workpart material properties and constraints.

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(A)

(B)

(C)

(D)

(E)

(F)

(G) (H) Figure 1: Variation of output responses with process parameters (A) Cutting Force – 0.8mm (B) Temperature – 0.8mm (C) Tool Wear – 0.8mm (D) Surface Finish – 0.8mm (E) Cutting Force – 0.4mm(F) Temperature – 0.4mm (G) Surface Finish – 0.4mm (H) Tool Wear – 0.4mm The trend followed in tool wear is very same as that of temperature in both machining before and after modification of insert. It is because when the temperature at the contact interface region rises, the thermal softening and deformation induces the insert to wear off. The surface roughness trend varies to the contrary before and after the modification of the insert. The cutting force and temperature values have comparatively fallen to a superior degree after the modification of the insert. The tool wear is also comparatively reduced to superior degree because of the point contact interface load and temperature induced was reduced.

Taguchi Analysis Taguchi analysis is used to determine better optimum conditions wherein the input constraints will induce better output responses. This is done by controlling the signal to noise ratio. It is mainly aimed at minimization of the output responses with respect to noise factors of the setup. The control factors taken for the analysis are the Impact Factor (JCC): 8.8746

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machining constraints like machining speed, feed and depth of cut which mainly affect th e turning of these super alloys [12].

Analysis of S/N Ratio

(A)

(B)

(C)

(D)

(E)

(F)

(G) (H) Figure 2: Variation of S/N ratios with process parameters (A) Cutting Force – 0.8mm (B) Temperature – 0.8mm (C) Surface Finish – 0.8mm (D) Tool Wear – 0.8mm (E) Cutting Force – 0.4mm(F) Temperature – 0.4mm (G) Surface Finish – 0.4mm (H) Tool Wear – 0.4mm In Taguchi analysis Signal/Noise (S/N) ratio is considered as the best evaluation criteria to predict the effective input constraints to minimize the output responses. The appropriate cutting constraints and their level can be found using this ratio. In this work, optimal cutting constraints for better output responses are aimed. For best output, minimum output is needed. So, “Smaller the Better” option is used to determine the S/N ratio by converting the output values into equivalent S/N rates [13]. Figure 2 show the variation of S/N ratios before and after modification of the insert. From figure 2, it is evident that before modification of the insert, with respect to cutting force the S/N ratio is found to be maximum at 60m/min, 0.1mm/rev and 1.5mm and after the modification, the maximum values are found at 60m/min, 0.1mm/rev and www.tjprc.org

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0.5mm where it is predicted to have better values. In case of temperature, the prediction of better result may happen at 100m/min, 0.1mm/rev and 0.5mm in case of 0.8mm insert and at 60m/min, 0.1mm/rev and 0.5mm for 0.4mm because of high S/N rates. While we consider the case of exterior quality of workpiece, the S/N ratio is established to be high 80m/min, 0.1mm/rev and 1mm for 0.8mm insert and the same is high at 60m/min, 0.1mm/rev and 0.5mm for 0.4mm wherein it is predicted to have better responses. Similarly tool wear is predicted to have better responses at 60m/min, 0.15mm/rev and 0.5mm for 0.8mm insert and 60m/min, 0.1mm/rev and 1mm for 0.4mm insert.

Confirmation Tests – Taguchi Analysis Confirmation tests are conducted to validate the predictions of Taguchi analysis and check the optimization. An experiment is executed on the super alloy by means of the ideal constraints, for each ideal constraint a mean of four outputs are taken out to catch the forecast value. The confirmation tests for machining before and after modification of insert is shown in Table 3 and Table 4 respectively. From these tables, it is confirmed that in all the predicted conditions, better output responses and optimum responses are achieved.

Response Cutting Force (N) Temperature (oC) Surface Roughness (µm) Tool Wear (µm)

Response Cutting Force (N) Temperature (oC) Surface Roughness (µm) Tool Wear (µm)

Table 3: Confirmation Tests – Insert – Nose Radius 0.8mm.

Optimal Conditions Cutting Feed Rate Depth of Cut Speed Predicted Experimental (mm/rev) (mm) (m/min) Value Value 60 0.1 1.5 582.22 521 100 0.1 0.5 509 372 80

0.1

1

0.77

0.2675

60

0.15

0.5

447.67

304

Table 4: Confirmation Tests – Insert – Nose Radius 0.4mm Optimal Conditions Cutting Feed Rate Depth of Cut Speed Predicted Experimental (mm/rev) (mm) (m/min) Value Value 60 0.1 0.5 527.78 423 60 0.1 0.5 452 235 60

0.1

0.5

0.603

0.126

60

0.1

1

409.55

234

Grey Relation Analysis In multi output project, the consequence and connotation between various constraints are complex and not vivid. This is named as grey which indicates deprived and inexact data. In this situation to optimize the same, Grey relational analysis is used which is done by normalizing the experimental data, finding the deviation and grey relation coefficient from which ranking is based on grey relational grade.

Grey Relation Analysis – Insert before Modification The grey relation of the output responses before modifications is done in following steps in Table 5 and 6. The normalized values of experimental output responses are displayed in Table 5. The deviation sequence, the grey relation coefficient and grade with corresponding rank are given in Table 6 for the outputs while machining with 0.8mm radius before modification.

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Table 5: Grey Relational Analysis - Nose radius 0.8mm – Experimental Values & Normalization Cutting Speed (m/min) 60 60 60 80 80 80 100 100 100

Feed Rate (mm/ rev) 0.1 0.15 0.2 0.1 0.15 0.2 0.1 0.15 0.2

Depth of Cut (mm) 0.5 1 1.5 1 1.5 0.5 1.5 0.5 1

Cutting Force (N) 526 534 558 561 576 583 601 634 667

Experimental Values Tempe Surface rature Roughness (oC) (µm) 382 0.6475 561 1.1654 534 1.2823 665 0.2686 464 0.4624 526 1.0582 434 0.6989 452 0.4788 563 0.8821

Tool Wear (µm) 310.66 471.64 466.44 631.14 376.43 465.32 362.49 408.94 535.96

Cutting Force (N) 1 0.9433 0.7730 0.7518 0.6454 0.5957 0.4681 0.2340 0

Normalization Tempe Surface rature Roughnes (oC) s (µm) 1 0.6262 0.3675 0.1153 0.4629 0 0 1 0.7102 0.8088 0.4912 0.2211 0.8163 0.5755 0.7527 0.7926 0.3604 0.3948

Tool Wear (µm) 1 0.4977 0.5139 0 0.7948 0.5174 0.8383 0.6933 0.2970

Table 6: Grey Relational Analysis - Nose radius 0.8mm – Deviation Sequence & Grey Relation Coefficient Cutting Force (N) 0 0.0567 0.2270 0.2482 0.3546 0.4043 0.5319 0.7660 1

Deviation Sequence Tempe Surface rature Roughness (oC) (µm) 0 0.3738 0.6325 0.8847 0.5371 1 1 0 0.2898 0.1912 0.5088 0.7789 0.1837 0.4245 0.2473 0.2074 0.6396 0.6052

Tool Wear (µm) 0 0.5023 0.4861 1 0.2052 0.4826 0.1617 0.3067 0.7030

Cutting Force (N) 1 0.8981 0.6878 0.6682 0.5851 0.5529 0.4845 0.3950 0.3333

Figure 3: Variation of Means of GRG (0.8mm)

Source Cutting Speed (m/min) Feed (mm/rev) Depth of Cut (mm) Error Total

Grey Relation Coefficient Tempe Surface rature Roughness (oC) (µm) 1 0.5722 0.4415 0.3611 0.4821 0.3333 0.3333 1 0.6331 0.7234 0.4956 0.3910 0.7313 0.5408 0.6690 0.7069 0.4388 0.4524

Tool Wear (µm) 1 0.4988 0.5071 0.3333 0.7090 0.5089 0.7556 0.6198 0.4156

Grey Relation Grade

Rank

0.8931 0.5499 0.5026 0.5837 0.6626 0.4871 0.6281 0.5977 0.4100

1 6 7 5 2 8 3 4 9

Figure 4: Variation of S/N of GRG (0.8mm)

Table 7: Anova Analysis - Nose Radius 0.8mm DF Seq SS Contribution Adj SS Adj MS 2 0.01672 11.08% 0.01672 0.008359 2 0.08362 55.41% 0.08362 0.041809 2 0.03165 20.98% 0.03165 0.015827 2 0.01891 12.53% 0.01891 0.009453 8 0.1509 100.00%

F-Value 0.88 4.42 1.67

P-Value 0.531 0.184 0.374

From Table 6, it is vibrant that the experiment state of 60m/min, 0.1mm/rev and 0.5mm depth exhibited a healthier rank and grade out of nine conditions. Figure 3 and 4 displays the dissimilarity of average GRG values and www.tjprc.org

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S/N ratios with admiration to machining constraints. From Figure 4, it is bright that it is foretold that cutting speed at level 1, feed rate at level 1 and depth of cut at level 1 will give near optimal output responses for 0.8mm insert before modification. After ANOVA analysis in Table 7, it is clear that 55.41% of influence is from the machining constraint of feed in the case of machining before modification. The validation experiment of the above foretold condition is given in table 11.

GREY RELATION ANALYSIS – INSERT AFTER MODIFICATION The grey relation of the output responses after modifications is done in following steps in Table 8 and 9.

Table 8: Grey Relational Analysis - Nose radius 0.4mm – Experimental Values & Normalization Cutting Speed (m/min)

Feed Rate (mm/r ev)

Depth of Cut (mm)

60 60 60 80 80 80 100 100 100

0.1 0.15 0.2 0.1 0.15 0.2 0.1 0.15 0.2

0.5 1 1.5 1 1.5 0.5 1.5 0.5 1

Cutting Force (N) 432 475 492 521 536 576 564 573 581

Experimental Values Tempe Surface rature Roughness (oC) (µm) 237 0.1273 407 0.635 392 1.045 506 0.4086 521 0.733 435 0.6576 485 0.4832 653 0.6448 432 0.6954

Tool Wear (µm) 236.03 342.54 375.94 420.54 441.11 431.71 420.3 602.05 415.77

Cutting Force (N) 1 0.7114 0.5973 0.4027 0.3020 0.0336 0.1141 0.0537 0

Normalization Tempe Surface rature Roughnes (oC) s (µm) 1 1.0000 0.5913 0.4468 0.6274 0 0.3534 0.6935 0.3173 0.3400 0.5240 0.4221 0.4038 0.6122 0 0.4361 0.5313 0.3810

Tool Wear (µm) 1 0.7090 0.6178 0.4959 0.4397 0.4654 0.4966 0 0.5089

Table 9: Grey Relational Analysis - Nose radius 0.8mm – Deviation Sequence & Grey Relation Coefficient Cutting Force (N) 0 0.2886 0.4027 0.5973 0.6980 0.9664 0.8859 0.9463 1

Deviation Sequence Tempe Surface rature Roughness (oC) (µm) 0 0.0000 0.4087 0.5532 0.3726 1 0.6466 0.3065 0.6827 0.6600 0.4760 0.5779 0.5962 0.3878 1.0000 0.5639 0.4688 0.6190

Tool Wear (µm) 0 0.2910 0.3822 0.5041 0.5603 0.5346 0.5034 1.0000 0.4911

Cutting Force (N) 1 0.6340 0.5539 0.4557 0.4174 0.3410 0.3608 0.3457 0.3333

Grey Relation Coefficient Tempe Surface rature Roughness (oC) (µm) 1 1 0.5503 0.4747 0.5730 0.3333 0.4361 0.6199 0.4228 0.4310 0.5123 0.4639 0.4561 0.5632 0.3333 0.4700 0.5161 0.4468

Tool Wear (µm) 1 0.6321 0.5667 0.4980 0.4716 0.4833 0.4983 0.3333 0.5045

Grey Relation Grade

Rank

1.0000 0.5728 0.5067 0.5024 0.4357 0.4501 0.4696 0.3706 0.4502

1 2 3 4 8 7 5 9 6

The normalized values of experimental output responses are displayed in Table 8. The deviation sequence, the grey relation coefficient and grade with corresponding rank are given in Table 9 for the outputs while machining with 0.8mm radius before modification. From Table 9, it is likely that the experiment state of 60m/min, 0.1mm/rev and 0.5mm depth exhibited a healthier rank and grade out of nine states. Figure 5 and 6 displays the distinction of average GRG values and S/N ratios with admiration to machining constraints.

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Multi Variate Optimization of the Influence of Edge Geometry in Orthogonal Machining of Ti6Al4V using PCBN Inserts by Taguchi and Grey Relational Analysis

Figure 5: Variation of Means of GRG (0.4mm)

17

Figure 6: Variation of S/N of GRG (0.4mm)

Table 10: Anova Analysis - Nose Radius 0.4mm Source Cutting Speed (m/min) Feed (mm/rev) Depth of Cut (mm) Error Total

DF 2 2 2 2 8

Seq SS 0.12337 0.07462 0.02967 0.04707 0.27473

Contribution 44.90% 27.16% 10.80% 17.13% 100.00%

Adj SS 0.12337 0.07462 0.02967 0.04707

Adj MS 0.06168 0.03731 0.01484 0.02354

F-Value 2.62 1.59 0.63

P-Value 0.276 0.387 0.613

From Figure 5, it is optimistic that it is foretold that cutting speed at level 1, feed rate at level 1 and last factor at level 1 will give near optimal output responses for 0.4mm insert after modification. After ANOVA analysis in Table 10, it is strong that 44.90% of influence is from the machining constraint of machining speed in the case of machining before modification. The validation experiment of the above foretold condition is given in Table 11.

Table 11: Confirmation Experiment – Grey Relational Analysis 0.8mm Insert

Output

0.4mm Insert

Prediction ExperimentPrediction Experiment

Cutting Force (N)

582.22

526

582.22

432

Temperature (oC)

509

382

509

237

Surface Roughness (µm)

0.77

0.6475

0.77

0.1273

447.67

310.66

447.67

236.03

Tool Wear (µm)

CONCLUSIONS 

The paper is mainly focused on the analysis of the impact of edge geometry on the output responses based on L9 Taguchi Array before and after modification of geometry of the insert from nose radius 0.8 to 0.4mm



In both circumstances, before and after modification of insert the cutting force rises with the rise in cutting speed and rise in feed but with respect to depth of cut, it increases only up to 1mm, then it reduces in case of 0.8mm insert, but it is vice versa when nose radius is at 0.4mm. The trend followed in tool wear with respect to machining constraints is very same as that of temperature in both machining before and after modification of insert. But surface roughness trend varies to the contrary before and after the modification of the insert.



The cutting force and temperature values have comparatively fallen to a larger level after the modification of the insert. The tool wear is also comparatively reduced to larger level because of the point contact interface load and temperature induced was reduced.

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

Taguchi analysis is conceded out to find the optimal conditions for which the output retorts is minimized.

Confirmation tests were steered to validate the foretold to optimum parameters. From Grey Relational Analysis, it is foretold that machining speed, feed rate and depth of cut at level 1 will give near best output retorts for both before and after modification of insert and confirmation tests were led to confirm the same. From ANOVA analysis, 55.41% of contribution is from the machining constraint of feed rate in the case of machining before modification and 44.90% of contribution is from the process parameter of machining speed in the case of machining after modification of insert.

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10. Sarvesh Kumar Mishra, Sudarsan Ghosh, Sivanandam Aravindan (2020). Machining performance evaluation of Ti6Al4V alloy with laser textured tools under MQL and nano-MQL environments. Journal of Manufacturing Processes, 53, 174–189 11. Adam D. Lampropoulos, Angelos P. Markopoulos and Dimitrios E. Manolakos (2019). Modeling of Ti6Al4V Alloy Orthogonal Cutting with Smooth Particle Hydrodynamics: A Parametric Analysis on Formulation and Particle Density. Metals, 9, 388 12. T. Tamizharasan, N. Senthilkumar, V. Selvakumar, S. Dinesh (2019). Taguchi’s methodology of optimizing turning parameters over chip thickness ratio in machining P/M AMMC. SN Applied Sciences, 1, 160 13. Harun Akkus, Harun Yaka (2018). Optimization of Turning Process by Using Taguchi Method. Journal of Science, 22, 1444-1448

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Multi Variate Optimization of the Influence of Edge Geometry in Orthogonal Machining of Ti6Al4V using PCBN Inserts by Taguchi and Grey Relational Analysis

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14. M. Bhengra, R. Davis, R. Lakra, A. Dungdung & A. Bakhla, “A Parametric Experimental Design Study of Abrasive Water Jet Machining”, International Journal of Mechanical Engineering (IJME), Vol. 5, Issue 4, pp. 1-8 15. Digvijay Kushwaha, Raiv Ranjan, Vijendra Kumar Kushawaha & Mohammad Tariq, 201C Evaluation and Optimization of Cutting Parameters for Turning of En-8 Steel: A Taguchi Approach”, International Journal of Mechanical Engineering (IJME), Vol. 6, Issue 4, pp. 35-44 16. Mohammed Yunus & Mohammad S.Alsoufi, “A Statistical Analysis of Joint Strength of Dissimilar Aluminium Alloys Formed

by Friction Stir Welding Using Taguchi Design Approach, Anova for the Optimization of Process Parameters”, IMPACT: International Journal of Research in Engineering & Technology (IMPACT: IJRET), Vol. 3, Issue 7, pp. 63-70

17. M. M. Banubakode, A. C. Gangal & A. D. Shirbhate, “Taguchi Method for Improving Powder Coating Process – A Case Study”, BEST: International Journal of Management, Information Technology and Engineering (BEST: IJMITE), Vol. 1, Issue 3, pp. 167-180

AUTHOR PROFILE

V. Veeranaath is currently working as Assistant Professor in Department of Mechanical Engineering, Faculty of Engineering and Technology, SRM Institute of Science of Technology for the past 5 years. He completed B.Tech (Mechanical Engineering) in Pondicherry Engineering College and M.Tech (Computer Integrated Manufacturing) from SRM University. His research interests are Tool Engineering, Optimization and Nano composites. He is a Life time member in International Association of Engineers (IAENG) and Associate Member in Institution of Engineers (IEI).

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