Main Article Content
Abstract
Due to the widespread use of highly automated machine tools in the metal cutting industry, manufacturing requires highly reliable models and methods for the prediction of output performance in the machining process. The prediction of optimal manufacturing conditions for good surface finish and dimensional accuracy plays a very important role in process planning. In the steel turning process the tool geometry and cutting conditions determine the time and cost of production which ultimately affect the quality of the final product. In the present work, experimental investigations have been conducted to determine the effect of the tool geometry (effective tool nose radius) and metal cutting conditions (cutting speed, feed rate and depth of cut) on surface finish during the turning of EN-31 steel. First and second order mathematical models are developed in terms of machining parameters by using the response surface methodology on the basis of the experimental results. The surface roughness prediction model has been optimized to obtain the surface roughness values by using LINGO solver programs. LINGO is a mathematical modeling language which is used in linear and nonlinear optimization to formulate large problems concisely, solve them, and analyze the solution in engineering sciences, operation research etc. The LINGO solver program is global optimization software. It gives minimum values of surface roughness and their respective optimal conditions.
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References
- Abhang LB, Hameedullah M (2010), Analysis of surface roughness by turning process using statistical method. Proceedings of the Int. Conf. on Advances in Industrial Engineering Application Chennai, India 138-146.
- Abhang LB, Hameedullah M (2010), A predictive model for tool wear rate in metal cutting using response surface methodology. Proceedings of the Int. Conf. on Advances in Industrial Engineering Applications Chennai, India 171-180.
- Aslan E, Camuscu N, Birgoren B (2007), Design optimization of cutting parameters when turning hardened AISI4140 steel with AL2O3 + TiCN mixed ceramic tool. Materials and Design 28:1618-1622.
- Birdie MA (1993), Surface roughness model for turning grey C.I. (154BHN). Proceedings of Institution of Mechanical Engineering, Part B, J. of Engineering Manufacture 207:43-54.
- Chen W (2000), Cutting forces and surface finish when machining medium hardness steel using CBN tools. Int. J. of Machine Tools and Manufacture 4(1):455-466.
- Dilbag R (2007), Optimization of tool geometry and cutting parameters for hard turning. J. of Materials and Manufacturing Process 22:15-21
- Feng CX, Wang X (2002), Development of empirical models for surface roughness prediction in finish turning. The Int. J. of Advanced Manufacturing Technology 20:348-356.
- Hasegawa MA, Seireg RA, Lindberg (1976), Surface roughness model for turning. Int. J. of Tribology 8(2):285-289.
- Konig W, Komanduri R, Tonshoff HK, Ackerson HG (1984), Machining of hard materials. Annals of CIRP 33(2):417-427.
- Kopac J, Bahor M, Sokovice M (2002), Optimal machining parameters for achieving the desired surface roughness in fine turning of cold preformed steel work piece. Int. J. of Machine tools and Manufacture 42:707-716.
- Manna A, Bhattacharyya B (2004), Investigation for optimal parametric combination for achieving better surface finish during turning of Al/Sic-MMC. Int. J. of Advanced Manufacturing Technology 23:658-665.
- Montgomery DC (1991), Design and analysis of experiment's. Third edition, John Wiley and Sons, New York, USA 521-568.
- Mehrban D,Naderi V, Panahizadeh H, Moslemi N (2008), Modeling of tool life in turning process using experimental method. Int. J. of Material Forming Springer/ESA Form 1-4.
- Nalbant M, Gokkaya H, Sur G (2007), Application of taguchi method in the optimization of cutting parameters for surface roughness in turning. Materials and Design 28:1379-1385.
- Ozel T, Karpat Y (2005), Predictive modeling of surface roughness and tool wear in hard turning using regression and neural networks. Int. J. of Machine Tools and Manufacture 45:467-479.
- Suresh R, Deshmukh S (2002), A genetic algorithmic approach for optimization of surface roughness prediction model. Int. J. of machine-tools and manufactures 42:675-680.
- Sundaram RM, Lambert BK (1981), Mathematical models to predict surface finish in fine turning of steel. Part 1, Int. J. of Production Research 19:547-556.
- Wang X, Balaji AK, Jawahir IS (2002), Performance based optimal selection of cutting condition and cutting tools in multi-pass turning operations using genetic algorithms. Int. J. of Production Research 40(9):2050-2065.
- Yang WH, Tarng YS (1998), Design optimization of cutting parameters for turning operations based on the Taguchi method. J. of Materials Processing Technology 84:122-129.
References
Abhang LB, Hameedullah M (2010), Analysis of surface roughness by turning process using statistical method. Proceedings of the Int. Conf. on Advances in Industrial Engineering Application Chennai, India 138-146.
Abhang LB, Hameedullah M (2010), A predictive model for tool wear rate in metal cutting using response surface methodology. Proceedings of the Int. Conf. on Advances in Industrial Engineering Applications Chennai, India 171-180.
Aslan E, Camuscu N, Birgoren B (2007), Design optimization of cutting parameters when turning hardened AISI4140 steel with AL2O3 + TiCN mixed ceramic tool. Materials and Design 28:1618-1622.
Birdie MA (1993), Surface roughness model for turning grey C.I. (154BHN). Proceedings of Institution of Mechanical Engineering, Part B, J. of Engineering Manufacture 207:43-54.
Chen W (2000), Cutting forces and surface finish when machining medium hardness steel using CBN tools. Int. J. of Machine Tools and Manufacture 4(1):455-466.
Dilbag R (2007), Optimization of tool geometry and cutting parameters for hard turning. J. of Materials and Manufacturing Process 22:15-21
Feng CX, Wang X (2002), Development of empirical models for surface roughness prediction in finish turning. The Int. J. of Advanced Manufacturing Technology 20:348-356.
Hasegawa MA, Seireg RA, Lindberg (1976), Surface roughness model for turning. Int. J. of Tribology 8(2):285-289.
Konig W, Komanduri R, Tonshoff HK, Ackerson HG (1984), Machining of hard materials. Annals of CIRP 33(2):417-427.
Kopac J, Bahor M, Sokovice M (2002), Optimal machining parameters for achieving the desired surface roughness in fine turning of cold preformed steel work piece. Int. J. of Machine tools and Manufacture 42:707-716.
Manna A, Bhattacharyya B (2004), Investigation for optimal parametric combination for achieving better surface finish during turning of Al/Sic-MMC. Int. J. of Advanced Manufacturing Technology 23:658-665.
Montgomery DC (1991), Design and analysis of experiment's. Third edition, John Wiley and Sons, New York, USA 521-568.
Mehrban D,Naderi V, Panahizadeh H, Moslemi N (2008), Modeling of tool life in turning process using experimental method. Int. J. of Material Forming Springer/ESA Form 1-4.
Nalbant M, Gokkaya H, Sur G (2007), Application of taguchi method in the optimization of cutting parameters for surface roughness in turning. Materials and Design 28:1379-1385.
Ozel T, Karpat Y (2005), Predictive modeling of surface roughness and tool wear in hard turning using regression and neural networks. Int. J. of Machine Tools and Manufacture 45:467-479.
Suresh R, Deshmukh S (2002), A genetic algorithmic approach for optimization of surface roughness prediction model. Int. J. of machine-tools and manufactures 42:675-680.
Sundaram RM, Lambert BK (1981), Mathematical models to predict surface finish in fine turning of steel. Part 1, Int. J. of Production Research 19:547-556.
Wang X, Balaji AK, Jawahir IS (2002), Performance based optimal selection of cutting condition and cutting tools in multi-pass turning operations using genetic algorithms. Int. J. of Production Research 40(9):2050-2065.
Yang WH, Tarng YS (1998), Design optimization of cutting parameters for turning operations based on the Taguchi method. J. of Materials Processing Technology 84:122-129.