A Review Paper on Optimization of Turning on En31 Material by Taguchi Approach

IJSRD - International Journal for Scientific Research & Development| Vol. 4, Issue 05, 2016 | ISSN (online): 2321-0613

A Review Paper on Optimization of Turning on EN31 Material by Taguchi Approach Sumit M. Parmar1 Prof. Jaivesh D.Gandhi2 1 Student 2Assistant Professor 1,2 Department of Mechanical Engineering 1,2 Shree S’ad Vidya Mandal Institute of Technology, Bharuch, Gujarat, India Abstract— the purpose of this paper is to make an attempt to review the literature on optimization of process parameters for minimum surface roughness and maximum material removal rate in turning. The process parameters like spindle speed (rpm), feed (mm/rev) and depth of cut (mm) are taken into consideration.

Key words: Process Parameters, Turning Operation, Surface Roughness, MRR, Taguchi Method I. INTRODUCTION Turning is the removal of metal from the outer diameter of a rotating cylindrical work piece. Turning is used to reduce the diameter of the work piece, usually to a specified dimension, and to produce a smooth finish on the metal. Often the work piece will be turned so that adjacent sections have different diameters.

Fig. 1: Work Piece Turning is the machining operation that produces cylindrical parts. In its basic form, it can be defined as the machining of an external surface:  With the work piece rotating.  With a single-point cutting tool  With the cutting tool feeding parallel to the axis of the work piece  At a distance that will remove the outer surface of the work.

II. LITERATURE SURVEY Suresh Kumar [1], have worked to Optimized the cutting parameters (spindle speed, feed and depth of cut) in dry hard turning of en31 material with uncoated cemented carbide insert tool. Taguchi’s L9 orthogonal array was conducted to find out the lowest surface roughness. A L9 orthogonal Array and ANOVA are applied to study the performance of machining parameters. Tool material to be used is cemented carbide. Optimum parameters has been also found for improved Surface roughness and high material removal rate

during the machining of EN31.by the experiment found that feed rate is most significant factor for surface roughness and speed and depth of cut most significant factor for MRR. Hridaya Shanker Ram, Muzahidul Islam [2], have worked to investigating machining characteristics of en31steel under different conditions in turning A L18 orthogonal Array is applied to study the performance of machining parameters. Tool material to be used is HSS. The machining characteristics investigated are Thrust force (T.F), Feed force (F.F), Radial Force (R.F), Surface roughness (S.R), and Material Removal Rate (MRR). The results showed that the response variables were strongly influenced by the control factors (input parameters).also found the optimum level of parameter.

Vishal Francis, Ravi. S. Singh, Nikita Singh, Ali. R. Rizvi and Santosh Kumar [3], optimized the cutting parameters of mild steel (0.18% C) in turning to obtain the factors effecting the surface roughness and MRR. To study the influence of cutting parameters they applied ANOVA and Signal to Noise ratio. The cutting parameters like spindle speed, feed and depth of cut were taken into consideration. A total of 27 experiments were done which were designed according to Taguchi method. The experiments were performed by using HSS cutting tool in dry condition. For MRR the most significant factor was spindle speed whereas feed was the most significant factor for surface roughness.

Shunmugesh K., Panneerselvam K., Pramod M. and Amal George [4], studied the machining process in turning of 11sMn30 alloy using carbide tip insert in dry condition. The optimal settings for the cutting parameters were obtained. The three level cutting parameters were cutting speed, feed rate and depth of cut. The turning experiment was conducted using L27 orthogonal array in CNC turning centre stallion 200. The roughness values Ra and Rz were measured in Mitutoyu SJ210 surface roughness tester. The statistical analysis was done by MINITAB 17. It was found that the feed rate is the most significant factor to affect surface roughness other than cutting speed and depth of cut.

Taquiuddin Quazi and Pratik Gajanan More [5], utilized Taguchi method to optimize the surface roughness in turning EN8, EM31 and mild steels. The three levels turning parameters considered were cutting speed and feed rate. The tool grades considered were TN60, TP0500 and TT8020. The experiments were carried on Super cut 5 turning machine. The roughness were measured by Wyko NT9100 Optical Profiling System. The Taguchi method was designed and analysed by Minitab statistical 16. L9 orthogonal array was used for analysis of all the materials along with three cutting tools. It was observed that feed rate has highest effect on surface roughness for all the three alloys.

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A Review Paper on Optimization of Turning on EN31 Material by Taguchi Approach (IJSRD/Vol. 4/Issue 05/2016/100)

Rony Mohan, Josephkunju Paul C and George Mathew [6], optimized the machining parameters (cutting speed, feed rate and depth of cut) for lower surface roughness. AISI 52100 steel alloy also known as bearing steels were used for optimization. Carbide inserted cutting tool with nose radius 0.80 were used for machining.

Taguchi’s L9 orthogonal arrays were used to design the experiment. Contribution of each factor was analyzed by ANOVA. It was found that feed has significant effect on surface roughness.

Brajesh Kumar Lodhi and Rahul Shukla [7], attempted to optimize the surface roughness and MRR in machining AISI 1018 alloy with Titanium coated Carbide inserts. Among spindle speed, feed rate and depth of cut the optimal setting was obtained. Taguchi’s L9 orthogonal array was used to experiment in a CNC lathe machine. The optimal MRR was obtained at the highest levels of all three factors. The minimum surface roughness was given at level 1, 1and 2 of each factor respectively. From ANOVA it was also obtained that the spindle speed is the most significant factor for MRR and surface roughness with 78.173% and 75.295% respectively D.V.V., Krishan Prasad [8], conducted full factorial design consisting of 243 experiments considering three machining parameters and two tool geometrical parameters to determine the impact of these parameters on surface roughness. The machining parameters were speed, feed and depth of cut whereas the tool geometrical parameters were back rake angle and side rack angle with three levels each.

The metal used for turning was mild steel with HSS cutting tool. It was found that feed is the only significant factor during this experiment H.M. Somashekara and N. Lakshmana Swamy [9], obtained an optimal setting for turning Al6351-T6 alloy for optimal surface roughness. A model was generated for optimal surface roughness using regression technique. The turning parameters considered were speed, feed and depth of cut with three levels each. L9 orthogonal array was implemented for the experiment. The roughness measure was done with three repetitions. The results found between regression model and experimental values were having error less than 2%. From ANOVA and S/N ratio, cutting speed was found to be highest significant parameter followed by feed and depth of cut.

Upinder Kumar Yadav, Deepak Narang and Pankaj Sharma Attri, [10], enquired the effect of machining parameters (speed, feed and depth of cut) on optimization of surface roughness in turning AISI 1045 steel alloy. The experiments were conducted on stallion 100HS CNC lathe using Taguchi’s L27 orthogonal array. From ANOVA it was found that feed has the maximum contribution of 95.23% on the surface roughness than cutting speed. Using the predictive equation the predicted value of optimum surface roughness at the optimal conditions was found to be 0.89μm whereas the calculated response was 0.93μm. Therefore the error between them comes out to be only 4.4%. So a good agreement was obtained between them. The results were evaluated by MINITAB 16 software.

Nitin Sharma, Shahzad Ahmad, Zahid A. Khan and Arshad Noor Siddiquee [11], applied L18 orthogonal array to optimize the surface roughness in turning. ANOVA and signal to noise ratio were applied to study the performance

characteristics in turning AISI 410 steel bars using TiN coated P20 and P30 cutting tool. The cutting parameters considered were insert radius, depth of cut, feed and cutting speed. It was found that the insert radius and feed rate has significant effect on surface roughness with 1.91% and 92.74% contribution respectively.

Rahul Davis and Mohamed Alazhari [12], worked to optimize the cutting parameters (spindle speed, feed and depth of cut) in dry turning of mild steel with 0.21% C and 0.64% Mn with a HSS cutting tool. Taguchi’s L27 orthogonal array was conducted to find out the lowest surface roughness. ANOVA and Signal to Noise ratio were utilized to find out the performance characteristics. Among the three cutting parameters only feed was found to be significant D. Lazarevic, M. Madic, P. Jankovic, A. Lazarevic [13], discusses the use of Taguchi method for minimizing the surface roughness in turning polyethylene. The influence of four cutting parameters, cutting speed (65.03, 115.61, 213.88 m/min),feed rate (0.049, 0.098, 0.196 mm/rev), depth of cut (1,2,4 mm) and tool nose radius (0.4,0.8 mm) on average surface roughness (Ra) was analyzed on the basis of the standard L27 Taguchi orthogonal array. The experimental results were then collected and analyzed with the help of the commercial software package MINITAB. Based on the analysis of means (ANOM) and analysis of variance (ANOVA), the optimal cutting parameter settings are determined, as well as level of importance of the cutting parameters. ANOVA results indicate that the feed rate is far the most significant parameter, followed by tool nose radius, and cutting speed, whereas the influence of depth of cut is negligible. Figure 19 shows % contribution for surface roughness. The ANOVA resulted in less than 10% error indicating that the interaction effect of process parameters is small. The optimum levels of the process parameters for minimum surface roughness are as follows: cutting speed 213.88 m/min, feed rate –0.049 mm/rev, depth of cut 2 mm, and tool nose radius 0.8 mm. The machine used for the experiments was the universal lathe machine Potisje PAC30.

Cutting tool was SANDVIK coromant tool holder SVJBR 3225P 16 with inserts VCGX16 04 04-AL (H10) and VCGX 16 04 08-AL (H10). It was measured at three equally spaced positions around the circumference of the work piece using the profilometer Surftest Mitutoyo SJ-301.

Jitendra Verma, Pankaj Agrawal, Lokesh Bajpai [14], Experiment was designed using Taguchi method and 9 experiments were conducted by this process. Cutting speed is the only significant factor which contributes to the surface roughness i.e. 57.47 %. The second factor which contributes to surface roughness is the feed rate having 23.46 %. The third factor which contributes to surface roughness is the depth of cut having 16.27%. It is recommended from the above results that cutting of 18.30 to 15.78 m/min can be used to get lowest surface roughness.

Marinkovic Velibor and Madic Milos [15], presents the Taguchi robust parameter design for modeling and optimization of surface roughness in dry single-point turning of cold rolled alloy steel 42CrMo4/AISI 4140 using TiNcoated tungsten carbide inserts was presented. Three cutting parameters, the cutting speed (80, 110, 140 m/min), the feed rate (0.071, 0.196, 0.321 mm/rev), and the depth of cut (0.5, 1.25, 2 mm), were used in the experiment. Each of the other

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A Review Paper on Optimization of Turning on EN31 Material by Taguchi Approach (IJSRD/Vol. 4/Issue 05/2016/100)

parameters was taken as constant. The average surface roughness (Ra) was chosen as a measure of surface quality.

The experiment was designed and carried out on the basis of standard L27 Taguchi orthogonal array. The surface roughness was most affected by cutting speed. The impact of feed rate was somewhat smaller, while the influence of depth of cut was least pronounced. On the other side, in qualitative terms, the influence of feed rate and depth of cut on the surface quality was opposite in relation to cutting speed. In fact, while the increase of cutting speed caused better surface quality, the increase of feed rate and depth of cut led to the decrease of surface quality.

Ali Riza Motorcu [16], investigated the surface roughness in the turning of AISI 8660 hardened alloy steels by ceramic based cutting tools was in terms of main cutting parameters such as cutting speed, feed rate, depth of cut in addition to tool nose radius, using a statistical approach.

Machining tests were carried out with PVD coated ceramic cutting tools under different conditions. An Orthogonal Array, Signal-to-Noise ratio and Analysis of Variance were employed to find out the effective cutting parameters and nose radius on the surface roughness. The machine used for the turning tests was a John ford TC35 industrial type of Computer Numeric Control (CNC) lathe machine. The insert was coated using a PVD method. The coating substance took place on the mixed ceramic substrate and PVD-TiN coated mixed ceramic with a matrix of Al2O3 (70%): TiC (30%) +TiN. The insert types were SNGA 120408 and SNGA 120412. AISI 8660 is a high carbon, chromium-nickel molybdenum alloy steel with high hardness and strength and is suitable for springs and axle shafts. The work pieces were in the form of cylinders of 52 mm diameter and 220 mm length. The standard heat treatment process to specimens was applied under water condition and the average hardness measured was about 50 HRC. These bars are machined under dry condition. The equipment used for measuring the surface roughness was a surface roughness tester, MAHR Perthometer-M1 type of portable. The surface roughness measures used is the arithmetic mean deviation of the surface roughness of profile, Ra. In collecting the surface roughness data of the shaft with the surface profilometer, three measurements were taken along the shaft axis for each sample with the measurements being about 120° apart. Figure 20 shows factor levels and their interactions on Ra. The obtained results indicate that the feed rate was found to be the dominant factor among controllable factors on the surface roughness, followed by depth of cut and tool’s nose radius.

However, the cutting speed showed an insignificant effect.

Furthermore, the interaction of feed rate/depth of cut was found to be significant on the surface finish due to surface hardening of steel. Moreover, the second order regression model also shows that the predicted values were very close to the experimental one for surface roughness S. Thamizhmanii, S. Saparudin, S. Hasan [17], analysed the optimum cutting conditions to get the lowest surface roughness in turning SCM 440 alloy steel by using coated ceramic tool. Taguchi’s mixed level L18 orthogonal array was used. The results were analysed in Design-Expert software. It was found that depth of cut was a significant factor then feed in consideration of lowest surface finish.

E.D Kirby [18], use the application of the Taguchi parameter design method to optimizing the surface finish in a turning operation. This study was conducted using samples cut from a single length of 1-in diameter 6061-T6 aluminum alloy rod. The control parameters for this operation included: Spindle Speed, Depth of Cut, and Feed W.H Yang and Y.S Tang [19], carried out an experiment consist of eighteen combination on an engine lathe using tungsten carbide with the grade of P- 10 for the machining of S45C steel bars. The cutting parameters that have been selected are cutting speed, feed rate and depth of cut with the response variable, tool life and surface roughness. Result show that cutting speed and feed rate are the significant cutting parameters for affecting tool life, while the change of the depth of cut in the range has an insignificant effect on tool life. For surface roughness, all the cutting parameters have the significant effect. The confirmation experiments then were conducted to verify the optimal cutting parameters. The improvement of tool life and surface roughness from the initial cutting parameters to the optimal cutting parameters is about 250%.

H. K. Dave, L. S. Patel and H. K. Raval [20], studied on different materials like EN-8 and EN-31 in CNC turning process using TiN coated cutting tools. They selected inserts, work materials, speed, feed and DOC as machining parameters and Taguchi L8 orthogonal array.

ANOVA has shown that the depth of cut has significant role to play in producing higher MRR and insert has significant role to play for producing lower surface roughness Ilhan, Asilturk, Harun Akkus [21], have studied on optimizing turning parameters based on the Taguchi method to minimize surface roughness (Ra and Rz). Experiments have been conducted using the L9 orthogonal array in a CNC turning machine. Dry turning tests are carried out on hardened AISI 4140 (51 HRC) with coated carbide cutting tools. The statistical methods of signal to noise ratio (SNR) and the analysis of variance (ANOVA) are applied to investigate effects of cutting speed, feed rate and depth of cut on surface roughness. Results of this study indicate that the feed rate has the most significant effect on Ra and Rz. In addition, the effects of two factor interactions of the feed rate-cutting speed and depth of cut-cutting speed appear to be important. The developed model can be used in the metal machining industries in order to determine the optimum cutting parameters for minimum surface roughness.

Dr. C.J Rao, D. Nageswara Rao, P. Srihari [22], have studied the significance of influence of speed, feed and depth of cut on cutting force and surface roughness while working with tool made of ceramic with an Al2O3+TiC matrix (KY1615)and the work material of AISI 1050 steel (hardness of 484 HV). Experiments were conducted using John ford TC35 Industrial type of CNC lathe. Taguchi method (L27 design with 3 levels and 3 factors) was used for the experiments. Analysis of variance with adjusted approach has been adopted. The results have indicated that it is feed rate which has significant influence both on cutting force as well as surface roughness. Depth of cut has a significant influence on cutting force, but has an insignificant influence on surface roughness. The interaction of feed and depth of cut and the interaction of all the three cutting parameters have significant influence on cutting

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A Review Paper on Optimization of Turning on EN31 Material by Taguchi Approach (IJSRD/Vol. 4/Issue 05/2016/100)

force, whereas, none of the interaction effects are having significant influence on the surface roughness produced.

Wang M. Y. and Lan T. S [23], used Orthogonal Array of Taguchi method coupled with Grey Relational Analysis considering four parameters viz. speed, cutting depth, feed rate, tool nose run off etc. for optimizing three responses: surface roughness, tool wear and material removal rate in precision turning on an ECOCA-3807 CNC lathe. The MINITAB software was explored to analyze the mean effect of Signal-to-Noise (S/N) ratio to achieve the multi objective features. This study not only proposed an optimization approach using Orthogonal Array and Grey Relational Analysis but also contributed a satisfactory technique for improving the multiple machining performances in precision CNC turning with profound insight.

Singh H. and Kumar P [24], studied on optimization of feed force through setting of optimal value of process parameters namely speed, feed and depth of cut in turning of EN24 steel with TiC coated tungsten carbide inserts. The authors used Taguchi’s parameter design approach and concluded that the effect of depth of cut and feed in variation of feed force were affected more as compare to speed.

III. CONCLUSIONS From the above literature review it is observed that Taguchi method is used to minimize surface roughness and maximize material removal rate by optimizing process parameters like cutting speed, spindle speed, feed rate, depth of cut, etc. It is observed that Taguchi Method is easy and simple so that it is most widely used method for optimization. In optimization of surface roughness feed is found to be the most affecting factor and for material removal rate depth of cut and cutting speed are found to be most affecting factors.

ACKNOWLEDGMENT The authors would like to thank Principal, H.O.D and teaching staff of mechanical engineering department for providing their valuable guidance and overwhelming support to carrying out this work.

REFERENCES [1] Suresh Kumar," Surface Roughness And Material Removal Rate Optimization of Uncoated Carbide Inserts In Dry Hard Turning of EN31 Steel ", Indian Journal of Applied Research, June 2015, pp. 241 - 243.

[2] Haridaya Shanker Ram, Muzhadil Islam," Investigating Machining Characteristics of EN31 Steel Under Different Conditions In Turning”, International Journal for Technological Research in Engineering, July 2015, pp. 2897- 2900.

[3] Vishal Francis, Ravi S. Singh, Nikita Singh, Ali R Rizvi & Santosh Kumar,” Application of Taguchi Method and ANOVA in Optimization of Cutting Parameters for Material Removal Rate and Surface Roughness in Turning Operation”, International Journal of Mechanical Engineering Technology Volume 4,June 2014,pp 47-53.

[4] Shunmugesh K, Panneerselvam K, Pramod M. & Amul George" Optimization of CNC Turning Parameters

With Carbide Tool For Surface Roughness Analysis Using Taguchi Analysis", Research Journal of Engineering Sciences, Vol. 3, June 2014, pp. 1 - 7.

[5] Taquiuddin Quazi and Pratik Gajanan More, “Optimization of Turning Parameters Such as Speed Rate, Feed Rate, Depth of Cut for Surface Roughness

by Taguchi Method”, Asian Journal of Engineering and Technology Innovation, Volume 2, March 2014, pp. 524 [6] Rony Mohan, Josephkunju Paul C and George Mathew, “Optimization of Surface Roughness of Bearing Steel during CNC Hard Turning Process”, International Journal of Engineering Trends and Technology, Volume 17, Nov 14, pp. 173- 175 [7] Brajesh Kumar Lodhi and Rahul Shukla, “Experimental Analysis on Turning parameters for Surface roughness and MRR”, Journal of Emerging Technologies and Innovative Research, Volume 1, Nov 2014, pp. 554 557 [8] D.V.V. Krishan Prasad, “Influence of Cutting Parameters on Turning Process Using ANOVA Analysis”, Research Journal of Engineering Sciences, Vol. 2, September 2013, pp. 1-6 [9] H.M. Somashekara and N. Lakshmana Swamy, “Optimizing Surface Roughness in Turning Operation using Taguchi Technique and ANOVA”, International Journal of Engineering Science and Technology, Vol. 4 May 2012,pp. 1967 - 1973 [10] Upinder Kumar Yadav, Deepak Narang and Pankaj Sharma Attri, “Experimental Investigation And Optimization Of Machining Parameters For Surface Roughness In CNC Turning By Taguchi Method”, International Journal of Engineering Research and Applications, Vol. 2, July-August 2012, pp. 2060 2065.

[11] Nitin Sharma, Shahzad Ahmad, Zahid A. Khan and Arshad Noor Siddiquee, “Optimization of Cutting Parameters for Surface Roughness in Turning”, International Journal of Advanced Research in Engineering and Technology, Volume 3, January- June 2012,pp. 86 - 96 [12] Rahul Davis and Mohamed Alazhari, “Optimization of Cutting Parameters in Dry Turning Operation of Mild Steel”, International Journal of Advanced Research in Engineering and Technology, Volume 3, JulyDecember 2012, pp. 104-110 [13] D. Lazarevic, M. Madic, P. Jankovic, A. Lazarevic ," Cutting Parameters Optimization for Surface Roughness in Turning Operation of Polyethylene (PE) Using Taguchi Method ", Tribology in Industry Vol. 34, No 2 (2012) 68-73 [14] Jitendra Verma, Pankaj Agrawal, Lokesh Bajpai, "Turning Parameters Optimization for Surface Roughness of ASTM A242 Type-1 Alloys Steel by Taguchi Method ", International Journal of Advances in Engineering & Technology, March 2012.pp 255-261 [15] Marinkovic Velibor and Madic Milos “Optimization of Surface Roughness in Turning Alloy Steel by Using Taguchi Method ", Scientific Research and Essays Vol. 6, August, 2011, pp.3474-3484 [16] Ali Riza Motorcu, “The Optimization of Machining Parameters Using the Taguchi Method for Surface

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Roughness of AISI 8660 Hardened Alloy Steel”, Journal of Mechanical Engineering 2010, pp. 391-401 [17] S. Thamizhmanii, S. Saparudin, S. Hasan, “Analyses of Surface Roughness by Turning Process Using Taguchi Method”, Journal of Achievements in Materials and Manufacturing Engineering ,Volume 20 JanuaryFebruary 2007,pp.503-506 [18] E. Daniel Kirby,” A Parameter Design Study in a Turning Operation Using the Taguchi Method “The Technology Interface, 2006, pp.1-14 [19] W.H Yang, Y.S Trang, “Design Optimization of Cutting Parameters for Turning Operations Based on Taguchi Method”, Journal of Material Processing Technology, 1998, pp.122-129 [20] H. K. Dave, L. S. Patel and H. K. Raval, " Effect of Machining Conditions on MRR and Surface Roughness During CNC Turning of Different Materials Using TiN Coated Cutting Tools – A Taguchi Approach ", International Journal of Industrial Engineering Computations 3 (2012) 925–930.

[21] Ilhan, Asilturk, Harun Akkus,. “Determining the effect of cutting parameters on surface roughness in hard turning using the Taguchi method”, Measurement 44, 2014, 1697-1704 [22] Dr. C.J Rao, D. Nageswara Rao, P. Srihari, “Influence of cutting parameters on cutting force and surface finish in turning operation”, Procedia Engineering 64, 2014, pp.-1405 -1415.

[23] Wang M. Y. and Lan T. S., “Parametric Optimization on Multi-Objective Precision Turning Using Grey Relational Analysis”. Information Technology Journal, Volume 7, 2008, pp.1072-1076 [24] Singh H. and Kumar P., “Optimizing Feed Force for Turned Parts through the Taguchi Technique”, Sadhana, Volume 31, Number 6, 2006, pp. 671–681.

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