Multi-objective optimization using genetic algorithms of multi-pass turning process

In this paper we present a multi-optimization technique based on genetic algorithms to search optimal cuttings parameters such as cutting depth, feed rate and cutting speed of multi-pass turning processes. Two objective functions are simultaneously optimized under a set of practical of machining constraints, the first objective function is cutting cost and the second one is the used tool life time. The proposed model deals multi-pass turning processes where the cutting operations are divided into multi-pass rough machining and finish machining. Results obtained from Genetic Algorithms method are presented in Pareto frontier graphic; this technique helps us in decision making process. An example is presented to illustrate the procedure of this technique.

[1]  Franci Cus,et al.  Approach to optimization of cutting conditions by using artificial neural networks , 2006 .

[2]  Franci Cus,et al.  Optimization of cutting process by GA approach , 2003 .

[3]  U. Zuperl,et al.  A hybrid analytical-neural network approach to the determination of optimal cutting conditions , 2004 .

[4]  G. C. Onwubolu,et al.  Optimization of multipass turning operations with genetic algorithms , 2001 .

[5]  A. K. Balaji,et al.  Performance-based optimal selection of cutting conditions and cutting tools in multipass turning operations using genetic algorithms , 2002 .

[6]  R. Venkata Rao,et al.  Multi-pass turning process parameter optimization using teaching–learning-based optimization algorithm , 2013 .

[7]  Yung C. Shin,et al.  Optimization of machining conditions with practical constraints , 1992 .

[8]  Singiresu S Rao,et al.  Determination of Optimum Machining Conditions—Deterministic and Probabilistic Approaches , 1976 .

[9]  J. Paulo Davim,et al.  A soft computing based optimisation of multi-pass turning processes , 2009 .

[10]  P. K. Kee.  Development of constrained optimisation analyses and strategies for multi-pass rough turning operations , 1996 .

[11]  S. S. K. Deepak,et al.  Applications of Different Optimization Methods for Metal Cutting Operation - A Review , 2012 .

[12]  N Alberti,et al.  Multipass machining optimization by using fuzzy possibilistic programming and genetic algorithms , 1999 .

[13]  Han Tong Loh,et al.  Optimization of cutting conditions for multi-pass turning operations using sequential quadratic programming , 1991 .

[14]  Goldberg,et al.  Genetic algorithms , 1993, Robust Control Systems with Genetic Algorithms.

[15]  R. V. Narang,et al.  Development of a framework to automate process planning functions and to determine machining parameters , 1993 .

[16]  G. K. Lal,et al.  Determination of optimal subdivision of depth of cut in multipass turning with constraints , 1995 .

[17]  Ali R. Yildiz,et al.  Hybrid Taguchi-Harmony Search Algorithm for Solving Engineering Optimization Problems , 2008 .

[18]  X. Wang,et al.  Optimization of multi-pass turning operations using genetic algorithms for the selection of cutting conditions and cutting tools with tool-wear effect , 2005 .

[19]  Hari Singh,et al.  Optimization of machining techniques — A retrospective and literature review , 2005 .

[20]  K. Ridgway,et al.  Selection of cutting tools and conditions of machining operations using an expert system , 2000 .

[21]  S. S. K. Deepak.  Cutting Speed and Feed Rate Optimization for Minimizing Production Time of Turning Process , 2012 .

[22]  David E. Goldberg,et al.  Genetic Algorithms in Search Optimization and Machine Learning , 1988 .

[23]  R. Saravanan,et al.  Optimization of multi-pass turning operations using ant colony system , 2003 .

[24]  J. Srinivas,et al.  Optimization of multi-pass turning using particle swarm intelligence , 2009 .

[25]  Y. S. Tarng,et al.  Optimization of turning operations with multiple performance characteristics , 1999 .

[26]  David E. Goldberg,et al.  Genetic algorithms and Machine Learning , 1988, Machine Learning.

[27]  E. Armarego,et al.  The Machining of Metals , 1969 .

[28]  N. R. Abburi,et al.  Multi-objective optimization of multipass turning processes , 2007 .

[29]  Du-Ming Tsai,et al.  A simulated annealing approach for optimization of multi-pass turning operations , 1996 .

[30]  Abdulrahman Al-Ahmari,et al.  Mathematical model for determining machining parameters in multipass turning operations with constraints , 2001 .

[31]  Ramón Quiza Sardiñas,et al.  Genetic algorithm-based multi-objective optimization of cutting parameters in turning processes , 2006, Eng. Appl. Artif. Intell..

[32]  松尾 哲夫.  The Machining of Metals, E.J.A.Armarego & R.H.Brown, Prentice-Hall, Inc., 1969 , 1984 .

[33]  Jun Wang,et al.  A neural network approach to multiple-objective cutting parameter optimization based on fuzzy preference information , 1993 .

[34]  J. S. Agapiou,et al.  The Optimization of Machining Operations Based on a Combined Criterion, Part 1: The Use of Combined Objectives in Single-Pass Operations , 1992 .