Application of taguchi method and anova in optimization of cutting
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The document describes a study that used Taguchi methods and analysis of variance (ANOVA) to optimize cutting parameters for material removal rate (MRR) and surface roughness during turning of mild steel. Experiments were conducted varying spindle speed, depth of cut, and feed rate as cutting parameters. ANOVA results showed that spindle speed had the greatest influence on MRR, while feed rate most significantly impacted surface roughness. Taguchi analysis indicated spindle speed provided the largest variation in MRR and feed rate produced the greatest variation in surface roughness. The optimal cutting parameters for maximum MRR and minimum surface roughness were determined.
Application of taguchi method and anova in optimization of cutting
- 1. International Journal of Mechanical Engineering and Technology (IJMET), ISSN 0976 – 6340(Print), ISSN 0976 – 6359(Online) Volume 4, Issue 3, May - June (2013) © IAEME 47 APPLICATION OF TAGUCHI METHOD AND ANOVA IN OPTIMIZATION OF CUTTING PARAMETERS FOR MATERIAL REMOVAL RATE AND SURFACE ROUGHNESS IN TURNING OPERATION Vishal Francis*, Ravi.S.Singh, Nikita Singh, Ali.R.Rizvi, Santosh Kumar Department of Mechanical Engineering, SSET, SHIATS, Allahabad – 211007, Uttar Pradesh, INDIA *Corresponding Author- Assistant Professor, Dept. of Mechanical Engineering, SSET, SHIATS, Allahabad – 211007, Uttar Pradesh, INDIA ABSTRACT The intend of this research work is to employ Taguchi method and Analysis of Variance to find out the influences of cutting parameters such as spindle speed, depth of cut and feed on material removal rate and surface roughness for their optimization. The experimental results obtained were analyzed to find out the most significant factor effecting MRR and surface roughness. Spindle speed was found to be the most significant parameter influencing material removal rate followed by feed and depth of cut in turning of mild steel (0.18% C). Feed rate was found to be the most influencing parameter in case of surface roughness. Keywords: Taguchi Method, ANOVA, Mild Steel, Material Removal Rate, Surface Roughness. INTRODUCTION Turning is one of the common metal cutting operations used for manufacturing of finished parts. The surface texture of the produced parts must fulfill the specified limitations so it is vitally important to produce parts with adequate surface finish in order to ensure product performance and reliability. The present work investigates the effect of cutting parameters on surface roughness and material removal rate, experiment was conducted with a INTERNATIONAL JOURNAL OF MECHANICAL ENGINEERING AND TECHNOLOGY (IJMET) ISSN 0976 – 6340 (Print) ISSN 0976 – 6359 (Online) Volume 4, Issue 3, May - June (2013), pp. 47-53 © IAEME: www.iaeme.com/ijmet.asp Journal Impact Factor (2013): 5.7731 (Calculated by GISI) www.jifactor.com IJMET © I A E M E
- 2. International Journal of Mechanical Engineering and Technology (IJMET), ISSN 0976 – 6340(Print), ISSN 0976 – 6359(Online) Volume 4, Issue 3, May - June (2013) © IAEME 48 HSS tool for turning of mild steel. Mild steel is a malleable and ductile material with a low tensile strength. The surface hardness can be increased through carburizing. The experiment was designed using Taguchi method, an L27 orthogonal array was selected and the three process parameter, depth of cut, feed and spindle speeds were varied to analyze the results obtained in order to find the optimal levels of the parameters for maximum material removal rate and minimum surface roughness. ANOVA was employed to find out significance of the different parameters on MRR and surface roughness. METHODOLOGY Essentially, traditional experimental design procedures are too complicated and not easy to use. A large number of experimental works have to be carried out when the number of process parameters increases. To solve this problem, the Taguchi method uses a special design of orthogonal arrays to study the entire parameter space with only a small number of experiments. This method uses a special set of arrays called orthogonal array. This standard array gives a way of conducting the minimum number of experiments which could give the full information of all the factors that affect the response parameter instead of doing all experiments. Analysis of variance was invented by Sir Ronald Fisher, who was a Statistician and Geneticist. He coined the phrase "analysis of variance," defined as "the separation of variance ascribable to one group of causes from the variance ascribable to the other groups. It is a statistical method of comparing means between groups; it computes the F-statistics, named after the inventor Fisher. The computed F value is compared with the critical F value to determine the significance of the parameters on the response. Variation observed (total) in an experimental attributed to each significant factor or interaction is reflected in percent contribution (P), which shows relative power of factor or interaction to reduce variation. MATERIALS AND METHOD A Mild steel rod of 150mm length and 32mm diameter was used for the experimental work. The chemical composition consists of (0.18%) carbon and (0.68%) manganese. The experiment was performed by high speed steel tool in dry cutting condition. Turning operation was performed 27 times in Sparko Engineering Workshop, Allahabad (U.P.) India. The total length of the work piece was divided into 4 equal parts and the surface roughness was measured for each 37.5mm length across the lay. Correspondingly MRR was calculated using standard relation: MRR= VFD mm3 /min (1) Where, MRR= Material Removal Rate, V= cutting velocity in mm/min, F= feed rate in mm/rev, D= depth of cut in mm. The three process parameters, viz. depth of cut, feed and spindle speeds were varied as shown in table 1.
- 3. International Journal of Mechanical Engineering and Technology (IJMET), ISSN 0976 – 6340(Print), ISSN 0976 – 6359(Online) Volume 4, Issue 3, May - June (2013) © IAEME 49 Table 1: Process parameters and their levels Process Parameters Level 1 Level 2 Level 3 Depth of cut (mm) 0.5 1.0 1.5 Feed (mm/rev) 0.08 0.20 0.32 Spindle speed (rpm) 780 1560 2340 REULTS AND ANALYSIS OF EXPERIMENTS Table 2 shows the values of the responses obtained from the experimental runs, designed by Taguchi method, the corresponding value of S/N Ratio is mentioned for each run. Table 2: Results for Experimental Trial Runs Exp. No. Depth of cut (mm) Feed (mm/rev) spindle speed (rpm) Surface Roughness (microns) Material Removal Rate (mm3 /min) S/N Ratio for Surface Roughness S/N Ratio for Material Removal Rate 1 0.5 0.08 780 97.5 3134976 -39.7801 129.925 2 0.5 0.08 1560 22.5 6269952 -27.0437 135.945 3 0.5 0.08 2340 100.0 9404928 -40.0000 139.467 4 0.5 0.20 780 95.0 783744 -39.5545 117.883 5 0.5 0.20 1560 105.0 15674880 -40.4238 143.904 6 0.5 0.20 2340 137.5 23512320 -42.7661 147.426 7 0.5 0.32 780 150.0 12539904 -43.5218 141.966 8 0.5 0.32 1560 110.0 25079808 -40.8279 147.986 9 0.5 0.32 2340 55.0 37619712 -34.8073 151.508 10 1.0 0.08 780 87.0 6269952 -38.7904 135.945 11 1.0 0.08 1560 95.0 12539904 -39.5545 141.966 12 1.0 0.08 2340 96.0 15674880 -39.6454 143.904 13 1.0 0.20 780 90.0 31349760 -39.0849 149.925 14 1.0 0.20 1560 96.0 12539904 -39.6454 141.966 15 1.0 0.20 2340 86.0 47024640 -38.6900 153.447 16 1.0 0.32 780 98.0 25079808 -39.8245 147.986 17 1.0 0.32 1560 100.0 50159616 -40.0000 154.007 18 1.0 0.32 2340 98.0 75239424 -39.8245 157.529 19 1.5 0.08 780 85.0 9404928 -38.5884 139.467 20 1.5 0.08 1560 96.7 18809856 -39.7085 145.488 21 1.5 0.08 2340 97.0 28214784 -39.7354 149.010 22 1.5 0.20 780 85.0 2351232 -38.5884 127.426 23 1.5 0.20 1560 93.0 47024640 -39.3697 153.447 24 1.5 0.20 2340 89.0 70536960 -38.9878 156.968 25 1.5 0.32 780 95.0 37619712 -39.5545 151.508 26 1.5 0.32 1560 100.0 37619712 -40.0000 151.508 27 1.5 0.32 2340 89.0 112859136 -38.9878 161.051
- 4. International Journal of Mechanical Engineering and Technology (IJMET), ISSN 0976 6340(Print), ISSN 0976 – 6359(Online) Volume 4, Issue 3, May A. Analysis of the S/N Ratio Taguchi method stresses the importance of signal – to – noise (S/N) ratio, resulting in minimization of quality characteristic variation due to uncontrollable parameter. The S/N Ratio is calculated using larger the better characteristics And the S/N Ratio for Surface roughness is calculated using smaller the better characteristics. Where n is the number of measurement in a trail/row and Yi is run/row. Table 3 and 4 shows the smaller the better for each level of the parameters Table 3: Response Table for Signal to Noise Ratios Larger is better Level Depth of cut (mm) 1 139.6 2 147.4 3 148.4 Delta 8.9 Rank 3 Table 4: Response Table for Signal to Noise Ratios Smaller is better Level Depth of cut (mm) 1 -38.75 2 -39.45 3 -39.28 Delta 0.70 Rank 3 A greater value of S/N ratio is always considered for better performance regardless of the category of the performance characteristics. The difference of S/N Ratio between level 1 and level 3 indicates the significance of the process parameters, greater the difference, greater will be the significance. Table 3 shows that the spindle speed contributes most significantly towards the delta valve (difference between maximum and minimum values) is highest (13.1) followed by feed (11.5) and depth of cut (8.9) Table 4 indicates that for surface roughness the parameter that had the most influence is feed with delta value of (1.61), followed by spindle speed (1.19) and depth of cut (0.70) International Journal of Mechanical Engineering and Technology (IJMET), ISSN 0976 6359(Online) Volume 4, Issue 3, May - June (2013) © IAEM 50 Taguchi method stresses the importance of studying the response variation using the noise (S/N) ratio, resulting in minimization of quality characteristic variation S/N Ratio is calculated using larger the better characteristics for MRR. the S/N Ratio for Surface roughness is calculated using smaller the better Where n is the number of measurement in a trail/row and Yi is the measured value in the Table 3 and 4 shows the Responses for Signal to Noise ratios of larger the better and the better for each level of the parameters. Response Table for Signal to Noise Ratios Larger is better (mm) Feed (mm/rev) Spindle speed 140.1 138.0 143.6 146.2 151.7 151.1 11.5 13.1 2 1 Response Table for Signal to Noise Ratios Smaller is better (mm) Feed (mm/rev) Spindle speed -38.09 -39.70 -39.68 -38.51 -39.71 -39.27 1.61 1.19 1 2 A greater value of S/N ratio is always considered for better performance regardless of the category of the performance characteristics. S/N Ratio between level 1 and level 3 indicates the significance of the process parameters, greater the difference, greater will be the significance. Table 3 shows that the spindle speed contributes most significantly towards the delta valve (difference between maximum and minimum values) is highest (13.1) feed (11.5) and depth of cut (8.9). Table 4 indicates that for surface roughness the parameter that had the most influence is feed with delta value of (1.61), followed by spindle speed (1.19) and depth of cut (0.70) International Journal of Mechanical Engineering and Technology (IJMET), ISSN 0976 – June (2013) © IAEME studying the response variation using the noise (S/N) ratio, resulting in minimization of quality characteristic variation for MRR. the S/N Ratio for Surface roughness is calculated using smaller the better measured value in the larger the better and Response Table for Signal to Noise Ratios Larger is better Spindle speed(rpm) 138.0 146.2 151.1 Response Table for Signal to Noise Ratios Smaller is better Spindle speed(rpm) 39.70 38.51 39.27 A greater value of S/N ratio is always considered for better performance regardless of S/N Ratio between level 1 and level 3 indicates the significance of the Table 3 shows that the spindle speed contributes most significantly towards MRR as the delta valve (difference between maximum and minimum values) is highest (13.1), Table 4 indicates that for surface roughness the parameter that had the most influence is feed with delta value of (1.61), followed by spindle speed (1.19) and depth of cut (0.70).
- 5. International Journal of Mechanical Engineering and Technology (IJMET), ISSN 0976 – 6340(Print), ISSN 0976 – 6359(Online) Volume 4, Issue 3, May - June (2013) © IAEME 51 Figure 1 and 2 shows the main effect plot for S/N ratio for MRR and surface roughness. It is clear from figure 1 that MRR increases on increasing the values of spindle speed, feed and depth of cut. The greatest variation found was due to spindle speed, MRR increases on increasing the spindle speed. In case of surface roughness, feed has the greatest variation; as there is a considerable increase of surface roughness on increasing the feed. 1 . 51 . 00 . 5 1 5 2 1 4 8 1 4 4 1 4 0 0 . 3 20 . 2 00 . 0 8 2 3 4 01 5 6 07 8 0 1 5 2 1 4 8 1 4 4 1 4 0 D E P TH O F C U T (mm) MeanofSNratios F E E D R A TE (mm/re v ) S P IN D LE S P E E D (rp m) M a in E f f e c ts P lo t f o r S N r a tio s Da ta M e a n s S ig n a l-to -n o is e : La r g e r is b e tte r Figure 1 Effect of depth of cut, feed and spindle speed on MRR 1 . 51 . 00 . 5 -3 8 . 0 -3 8 . 4 -3 8 . 8 -3 9 . 2 -3 9 . 6 0 . 3 20 . 2 00 . 0 8 2 3 4 01 5 6 07 8 0 -3 8 . 0 -3 8 . 4 -3 8 . 8 -3 9 . 2 -3 9 . 6 D E P T H O F C U T (mm) MeanofSNratios F E E D R A TE (mm/re v ) S P IN D LE S P E E D (rp m) M a i n E f f e c ts P lo t f o r S N r a tio s D a ta M e a n s S ig n a l- to -n o is e : S m a lle r is b e tte r Figure 2 Effect of depth of cut, feed and spindle speed on MRR B. Analysis of Variance (ANOVA) The response data obtained via experimental runs for MRR and surface roughness were subjected to ANOVA for finding out the significant parameters, at above 95% confidence level and the results of ANOVA thus obtained for the response parameters are illustrated in table 5 and 6.
- 6. International Journal of Mechanical Engineering and Technology (IJMET), ISSN 0976 – 6340(Print), ISSN 0976 – 6359(Online) Volume 4, Issue 3, May - June (2013) © IAEME 52 Table 5: ANOVA table for S/N Ratios for MRR Source DF SS MS F P Depth of cut(mm) 2 424.29 212.14 6.54 0.007 Feed (mm/rev) 2 631.84 315.92 9.73 0.001 Spindle speed (rpm) 2 793.97 396.98 12.23 0.000 Error 20 649.09 32.45 Total 26 2499.19 Table 6: ANOVA table for S/N Ratios for Surface Roughness Source DF SS MS F P Depth of cut(mm) 2 2.426 1.213 0.13 0.877 Feed (mm/rev) 2 15.327 7.663 0.83 0.449 Spindle speed (rpm) 2 6.547 3.273 0.36 0.705 Error 20 183.935 9.197 Total 26 208.235 From Table 5, one can observe that the spindle speed (p=0.000) is the most significant parameter having 31.77% effect on MRR followed by feed (p= 0.001) having 25.28% influence on the response whereas depth of cut (p=0.007) influences the response only by 16.98%. On comparing the percentage contribution of the process parameters on response from table 6 it was found that feed have 7.36% influence on surface roughness whereas spindle speed and depth of cut contributes less towards surface roughness. CONCLUSION The study discusses about the application of Taguchi method and ANOVA to investigate the effect of process parameters on MRR and surface roughness. From the analysis of the results obtained following conclusion can be drawn: - • Statistically designed experiments based on Taguchi method are performed using L27 orthogonal array to analyze MRR and surface roughness. The results obtained from analysis of S/N Ratio and ANOVA were in close agreement. • Optimal parameters for MRR are spindle speed 2340rpm; feed 0.32mm/rev and depth of cut 1.5mm. Whereas for surface roughness the optimal parameters found were spindle speed 1560rpm; feed 0.08mm/rev and depth of cut 0.5mm. • Spindle peed was found to be most significant parameter for MRR.
- 7. International Journal of Mechanical Engineering and Technology (IJMET), ISSN 0976 – 6340(Print), ISSN 0976 – 6359(Online) Volume 4, Issue 3, May - June (2013) © IAEME 53 REFERENCES 1. http://en.wikipedia.org/wiki/Carbon_steel 2. Rama Rao.S and Padmanabhan.G (2012), “Application of Taguchi method and ANOVA for metal removal rate in electrochemical machining of Al/5%SiC composites”, International journal of engineering research & Application. vol. 2, issue 3, pp. 192-197 3. Gulhane U.D., et. al. (2013), “Investigating the effect of machining parameters on surface roughness of 6061 Aluminum Alloy in end milling”, International journal of Mechanical Engineering and Technology. Vol.4, issue 2, pp. 134-140. 4. http://www.informatics-review.com/wiki/index.php/ANOVA. 5. J.Pradeep Kumar and P.Packiaraj (2012), “Effect of drilling parameters on surface roughness, Tool wear, Material removal Rate and hole diameter error in drilling of OHNS”, International Journal of Advanced Engineering Research and Studies. Vol. I/ Issue III/April-June, 2012/150-154. 6. K.Krishnamurthy and J.Venkatesh (2013), “ Assessment of surface roughness and material removal rate on machining of TIB2 reinforced Aluminum 6063 composites: A Taguchi’s approach”, International Journal of Scientific and Research Publications. Vol. 3, Issue 1. 7. Adeel H. Suhail, et. al. (2010). “Optimization of Cutting Parameters Based on Surface Roughness and Assistance of Workpiece Surface Temperature in Turning Process”, American J. of Engineering and Applied Sciences 3 (1): 102-108 8. Karin Kandananond (2009), “Characterization of FDB Sleeve Surface Roughness Using the Taguchi Approach”, European Journal of Scientific Research ISSN 1450- 216X Vol.33 No.2 , pp.330-337 © EuroJournals Publishing, Inc 9. Yigit Kazancoglu, et. al. (2011), “Multi Objective Optimization Of The Cutting Forces In Turning Operations Using The Grey-Based Taguchi Method” Original scientific article/Izvirni znanstveni ~lanek. MTAEC9 10. Puertas. I. Arbizu, C.J. Luis Prez (2003), “Surface roughness prediction by factorial design of experiments in turning processes”, Journal of Materials Processing Technology, 143- 144 390-396 11. B. S. Raghuwanshi (2009), A course in Workshop Technology Vol.II (Machine Tools), Dhanpat Rai & Company Pvt. Ltd. 12. 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 & Technology (IJARET), Volume 3, Issue 1, 2012, pp. 86 - 96, ISSN Print: 0976-6480, ISSN Online: 0976-6499. 13. R. R. Deshmukh and V. R. Kagade, “Optimization of Surface Roughness in Turning High Carbon High Chromium Steel by using Taguchi Method”, International Journal of Mechanical Engineering & Technology (IJMET), Volume 3, Issue 1, 2012, pp. 321 - 331, ISSN Print: 0976 – 6340, ISSN Online: 0976 – 6359. 14. Rahul Davis and Mohamed Alazhari, “Analysis and Optimization of Surface Roughness in Dry Turning Operation of Mild Steel”, International Journal of Industrial Engineering Research and Development (IJIERD), Volume 3, Issue 2, 2012, pp. 1 - 9, ISSN Online: 0976 - 6979, ISSN Print: 0976 – 6987.