AbstractThe experimental investigation of turning on EN8 steel of grade SAE (AISI) 1040 using HSS cutting tools was the focus of this work. The following study’s main goal was to apply the Taguchi method to ascertain how the machining parameters – feed, depth of cut, and cutting speed – affect the rate at which material is removed from the machined material. Finding the ideal machining parameters to optimize the material removal rate for the chosen tool and conducting a comparison analysis for cutting tools was the goal. A Design of Expert (DOE) was used to create the experiment matrix, which consisted of nine runs. A weighing machine was used to measure the material removed during machining. This device, which is made by SHINKO DENSHI Co. LTD. in Japan, has a 300-gram capacity with an accuracy of 0.001 gram. DJ 300S is the model number. For analysis, the data was assembled into MINITAB ® 17. The Taguchi approach was used to model and assess the relationship between the response variables (MRR) and the machining parameters.

Keywords:HSS cutting tools, MRR, MINITAB, EN8 steel, Taguchi method

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1. Antony J. Multi-response optimization in industrial experiments using Taguchi’s quality loss function and principal component analysis. Quality and Reliability Engineering International. 2000.
2. Ahmed SG. Development of a prediction model for surface roughness in finish turning of aluminium. Sudan Engineering Society Journal. 2006.
3. Abburi NR, Dixit US. A knowledge-based system for the prediction of surface roughness in turning process. Robotics and Computer-Integrated Manufacturing. 2006.
4. Al-Ahmari AMA. Predictive machinability models for a selected hard material in turning operations. J Mater Proc Tech. 2007;190:305–311.
5. Choudhury SK, Bartarya G. Role of temperature and surface finish in predicting tool wear using neural network and design of experiments‖. Int J Machine Tools and Manuf. 2003.
6. Chien WT, Tsai CS. The investigation on the prediction of tool wear and the determination of optimum cutting conditions in machining 17-4PH stainless steel. J Mater Proc Tech. 2003.
7. Biswas CK, Chawla BS, Das NS, Srinivas ER Tool wear prediction using neuro-fuzzy system. Institution of Engineers (India) Journal (PR), 2008;89:42–46.
8. Doniavi A, Eskanderzade M, Tahmsebian M. Empirical modeling of surface roughness in turning process of 1060 steel using factorial design methodology. J App Sci. 2007;7(17):2509–2513.
9. Datta S, Bandyopadhyay A, Pal PK. Application of taguchi philosophy for parametric optimization of bead geometry and HAZ width in submerged arc welding using mixture of fresh flux and fused slag. Internat J Adv Manuf Tech. 2008;36:689–698.
10. Datta S, Nandi G, Bandyopadhyay A, Pal PK. Application of PCA based hybrid Taguchi method for multi-criteria optimization of submerged arc weld: A case study. Int J Adv Manuf Tech. 2009. (Article In press). Available from: http://doi.org/10.1007/s00170-009-1976-0
11. Feng CX(Jack), Wang X. Development of empirical models for surface roughness prediction in finish turning. Int J Adv Manuf Tech. 2002;20:348–356.
12. Fnides B, Aouici H, Yallese MA. Cutting forces and surface roughness in hard turning of hot work steel X38CrMoV5-1 using mixed ceramic.
13. Mechanika, Fu P, Hope AD. A hybrid pattern recognition architecture for cutting tool condition monitoring. Tech Appl. 2008;24(4):548–558.
14. Kirby ED, Zhang Z, Chen JC. Development of an accelerometer based surface roughness prediction system in turning operation using multiple regression techniques. J Indu Tech. 2004;20(4):1–8.
15. Kohli A, Dixit US. A neural-network-based methodology for the prediction of surface roughness in a turning process. Int J Adv ManufTechnol. 2005;25:118–129.
16. Kumanan S, Saheb SKN, Jesuthanam CP. Prediction of machining forces using neural networks trained by a genetic algorithm. Institution of Engineers (India) J. 2006;7:11–15.
17. Kassab SY, Khoshnaw YK. The effect of cutting tool vibration on surface roughness of work piece in dry turning operation. Eng Technol. 2007;25(7):879–889.
18. Lin WS, Lee BY, Wu CL. Modeling the surface roughness and cutting force for turning. J Mater Proc Technol. 2001;108:286–293.
19. Lee SS. Chen JC. Online surface roughness recognition system using artificial neural networks system in turning operations. Int J Adv Manuf Technol. 2003;22:498–509.
20. Lan TS, Lo CY, Wang MY, Yen A-Y. Multi quality prediction model of CNC turning using back propagation network. Inf Technol J. 2008;7(6):911–917.
21. Mahmoud EAE, Abdelkarim HA. Optimum cutting parameters in turning operations using HSS cutting tool with 450 approach angle. Sudan Eng Scoeiety J. 2006;53(48):25–30.
22. Natarajan U, Arun P, Periasamy VM. On- line tool wear monitoring in turning by hidden markov model (HMM). Inst Eng (India) J (PR). 2007;87:31–35.
23. Özel T, Karpat Y. Predictive modeling of surface roughness and tool wear in hard turning using regression and neural networks. Int J Mach Tools Manuf. 2005;45:467–479.
24. Özel T, Karpat Y, Figueira L, Davim JP. Modeling of surface finish and tool flank wear in turning of AISI D2 steel with ceramic wiper inserts. J Mater Proc Technol. 2007;189:192–198.
25. Pal SK, Chakraborty D. Surface roughness prediction in turning using artificial neural network. Neural Comput Appl. 2005;14:319–324.
26. Reddy BS, Padmanabhan G, Reddy KVK. Surface roughness prediction techniques for CNC turning. Asian J Sci Res. 2008;1(3):256–264.
27. Su CT, Tong LI. Multi-response robust design by principal component analysis. Total Quality Management. 1997.
28. Suresh PVS, Rao PV, Deshmukh SG. A genetic algorithmic approach for optimization of surface roughness prediction model. Int J Mach Tools Manuf. 2002;42:675–680.
29. Singh H, Kumar P. Optimizing feed force for turned parts through the Taguchi technique. Sadhana. 2006;31( 6): 671–681.
30. Srikanth T, Kamala V. A real coded genetic algorithm for optimization of cutting parameters in turning IJCSNS. Int J Comp Sci Net Sec. 2008;8 (6): 189–193.
31. Sahoo P, Barman TK, Routara BC. Taguchi based practical dimension modeling and optimization in CNC turning. Adv Prod Eng Manag. 2008;3(4):205–217.
32. Shetty R, Pai R, Kamath V, Rao SS. Study on surface roughness minimization in turning of DRACs using surface roughness methodology and Taguchi under pressured steam jet approach. ARPN J Eng App Sci. 2008;3(1):59–67.
33. Thamizhmanii S, Saparudin S, Hasan S. Analysis of surface roughness by using Taguchi method. J Achiev Mater Manuf Eng. 2007;20(1–2):503–505.
34. Thamma R. Comparison between multiple regression models to study effect of turning parameters on the surface roughness. Proceedings of the 2008.
35. Walia RS, Shan HS, Kumar, P. Multi-response optimization of CFAAFM process through Taguchi method and utility concept. Mater Manuf Proc. 2006;21:907–914.
36. Wang MY, Lan TS. Parametric optimization on multi-objective precision turning using grey relational analysis. Inf Technol J. 2008;7:1072–1076.
37. Wannas AA. RBFNN model for prediction recognition of tool wear in hard turning. J Eng Applies Sci. 2008;3(10):780–785.
38. Zhou Q, Hong GS, Rahman M. A New Tool Life Criterion For Tool Condition Monitoring Using a Neural Network‖, Engineering Application Artificial Intelligence. 1995;8(5):579–588.
39. Zhong ZW, Khoo LP, Han ST. Prediction of surface roughness of turned surfaces using neural networks. Int J Adv Manuf Technol. 2006;28:688–693.
40. Bhattacharyya A. Metal cutting: Theory and practice. New Central Book Agency. 2006:495–501. ISBN: 81-7381-228-4.

AbstractThe experimental investigation of turning on EN8 steel of grade SAE (AISI) 1040 using HSS cutting tools was the focus of this work. The following study’s main goal was to apply the Taguchi method to ascertain how the machining parameters – feed, depth of cut, and cutting speed – affect the rate at which material is removed from the machined material. Finding the ideal machining parameters to optimize the material removal rate for the chosen tool and conducting a comparison analysis for cutting tools was the goal. A Design of Expert (DOE) was used to create the experiment matrix, which consisted of nine runs. A weighing machine was used to measure the material removed during machining. This device, which is made by SHINKO DENSHI Co. LTD. in Japan, has a 300-gram capacity with an accuracy of 0.001 gram. DJ 300S is the model number. For analysis, the data was assembled into MINITAB ® 17. The Taguchi approach was used to model and assess the relationship between the response variables (MRR) and the machining parameters.

Keywords:HSS cutting tools, MRR, MINITAB, EN8 steel, Taguchi method

🔒 This is a subscription article

Full text is available to subscribers and institutional members. Please choose an option below to access it.

1. Antony J. Multi-response optimization in industrial experiments using Taguchi’s quality loss function and principal component analysis. Quality and Reliability Engineering International. 2000.
2. Ahmed SG. Development of a prediction model for surface roughness in finish turning of aluminium. Sudan Engineering Society Journal. 2006.
3. Abburi NR, Dixit US. A knowledge-based system for the prediction of surface roughness in turning process. Robotics and Computer-Integrated Manufacturing. 2006.
4. Al-Ahmari AMA. Predictive machinability models for a selected hard material in turning operations. J Mater Proc Tech. 2007;190:305–311.
5. Choudhury SK, Bartarya G. Role of temperature and surface finish in predicting tool wear using neural network and design of experiments‖. Int J Machine Tools and Manuf. 2003.
6. Chien WT, Tsai CS. The investigation on the prediction of tool wear and the determination of optimum cutting conditions in machining 17-4PH stainless steel. J Mater Proc Tech. 2003.
7. Biswas CK, Chawla BS, Das NS, Srinivas ER Tool wear prediction using neuro-fuzzy system. Institution of Engineers (India) Journal (PR), 2008;89:42–46.
8. Doniavi A, Eskanderzade M, Tahmsebian M. Empirical modeling of surface roughness in turning process of 1060 steel using factorial design methodology. J App Sci. 2007;7(17):2509–2513.
9. Datta S, Bandyopadhyay A, Pal PK. Application of taguchi philosophy for parametric optimization of bead geometry and HAZ width in submerged arc welding using mixture of fresh flux and fused slag. Internat J Adv Manuf Tech. 2008;36:689–698.
10. Datta S, Nandi G, Bandyopadhyay A, Pal PK. Application of PCA based hybrid Taguchi method for multi-criteria optimization of submerged arc weld: A case study. Int J Adv Manuf Tech. 2009. (Article In press). Available from: http://doi.org/10.1007/s00170-009-1976-0
11. Feng CX(Jack), Wang X. Development of empirical models for surface roughness prediction in finish turning. Int J Adv Manuf Tech. 2002;20:348–356.
12. Fnides B, Aouici H, Yallese MA. Cutting forces and surface roughness in hard turning of hot work steel X38CrMoV5-1 using mixed ceramic.
13. Mechanika, Fu P, Hope AD. A hybrid pattern recognition architecture for cutting tool condition monitoring. Tech Appl. 2008;24(4):548–558.
14. Kirby ED, Zhang Z, Chen JC. Development of an accelerometer based surface roughness prediction system in turning operation using multiple regression techniques. J Indu Tech. 2004;20(4):1–8.
15. Kohli A, Dixit US. A neural-network-based methodology for the prediction of surface roughness in a turning process. Int J Adv ManufTechnol. 2005;25:118–129.
16. Kumanan S, Saheb SKN, Jesuthanam CP. Prediction of machining forces using neural networks trained by a genetic algorithm. Institution of Engineers (India) J. 2006;7:11–15.
17. Kassab SY, Khoshnaw YK. The effect of cutting tool vibration on surface roughness of work piece in dry turning operation. Eng Technol. 2007;25(7):879–889.
18. Lin WS, Lee BY, Wu CL. Modeling the surface roughness and cutting force for turning. J Mater Proc Technol. 2001;108:286–293.
19. Lee SS. Chen JC. Online surface roughness recognition system using artificial neural networks system in turning operations. Int J Adv Manuf Technol. 2003;22:498–509.
20. Lan TS, Lo CY, Wang MY, Yen A-Y. Multi quality prediction model of CNC turning using back propagation network. Inf Technol J. 2008;7(6):911–917.
21. Mahmoud EAE, Abdelkarim HA. Optimum cutting parameters in turning operations using HSS cutting tool with 450 approach angle. Sudan Eng Scoeiety J. 2006;53(48):25–30.
22. Natarajan U, Arun P, Periasamy VM. On- line tool wear monitoring in turning by hidden markov model (HMM). Inst Eng (India) J (PR). 2007;87:31–35.
23. Özel T, Karpat Y. Predictive modeling of surface roughness and tool wear in hard turning using regression and neural networks. Int J Mach Tools Manuf. 2005;45:467–479.
24. Özel T, Karpat Y, Figueira L, Davim JP. Modeling of surface finish and tool flank wear in turning of AISI D2 steel with ceramic wiper inserts. J Mater Proc Technol. 2007;189:192–198.
25. Pal SK, Chakraborty D. Surface roughness prediction in turning using artificial neural network. Neural Comput Appl. 2005;14:319–324.
26. Reddy BS, Padmanabhan G, Reddy KVK. Surface roughness prediction techniques for CNC turning. Asian J Sci Res. 2008;1(3):256–264.
27. Su CT, Tong LI. Multi-response robust design by principal component analysis. Total Quality Management. 1997.
28. Suresh PVS, Rao PV, Deshmukh SG. A genetic algorithmic approach for optimization of surface roughness prediction model. Int J Mach Tools Manuf. 2002;42:675–680.
29. Singh H, Kumar P. Optimizing feed force for turned parts through the Taguchi technique. Sadhana. 2006;31( 6): 671–681.
30. Srikanth T, Kamala V. A real coded genetic algorithm for optimization of cutting parameters in turning IJCSNS. Int J Comp Sci Net Sec. 2008;8 (6): 189–193.
31. Sahoo P, Barman TK, Routara BC. Taguchi based practical dimension modeling and optimization in CNC turning. Adv Prod Eng Manag. 2008;3(4):205–217.
32. Shetty R, Pai R, Kamath V, Rao SS. Study on surface roughness minimization in turning of DRACs using surface roughness methodology and Taguchi under pressured steam jet approach. ARPN J Eng App Sci. 2008;3(1):59–67.
33. Thamizhmanii S, Saparudin S, Hasan S. Analysis of surface roughness by using Taguchi method. J Achiev Mater Manuf Eng. 2007;20(1–2):503–505.
34. Thamma R. Comparison between multiple regression models to study effect of turning parameters on the surface roughness. Proceedings of the 2008.
35. Walia RS, Shan HS, Kumar, P. Multi-response optimization of CFAAFM process through Taguchi method and utility concept. Mater Manuf Proc. 2006;21:907–914.
36. Wang MY, Lan TS. Parametric optimization on multi-objective precision turning using grey relational analysis. Inf Technol J. 2008;7:1072–1076.
37. Wannas AA. RBFNN model for prediction recognition of tool wear in hard turning. J Eng Applies Sci. 2008;3(10):780–785.
38. Zhou Q, Hong GS, Rahman M. A New Tool Life Criterion For Tool Condition Monitoring Using a Neural Network‖, Engineering Application Artificial Intelligence. 1995;8(5):579–588.
39. Zhong ZW, Khoo LP, Han ST. Prediction of surface roughness of turned surfaces using neural networks. Int J Adv Manuf Technol. 2006;28:688–693.
40. Bhattacharyya A. Metal cutting: Theory and practice. New Central Book Agency. 2006:495–501. ISBN: 81-7381-228-4.