Optimization techniques for machining operations: a retrospective research based on various mathematical models

Simulated annealing, genetic algorithm, and particle swarm optimization techniques have been used for exploring optimal machining parameters for single pass turning operation, multi-pass turning operation, and surface grinding operation. The behavior of optimization techniques are studied based on various mathematical models. The objective functions of the various mathematical models are distinctly different from each other. The most affecting machining parameters are considered as cutting speed, feed, and depth of cut. Physical constraints are speed, feed, depth of cut, power limitation, surface roughness, temperature, and cutting force.

[1]  Indrajit Mukherjee,et al.  A review of optimization techniques in metal cutting processes , 2006, Comput. Ind. Eng..

[2]  Zhang Li-ping,et al.  Optimal choice of parameters for particle swarm optimization , 2005 .

[3]  R. Saravanan,et al.  A multi-objective genetic algorithm (GA) approach for optimization of surface grinding operations , 2002 .

[4]  L. C. Leung,et al.  Optimal machining conditions with costs of quality and tool maintenance for turning , 2001 .

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

[6]  P. Venkateswara Rao,et al.  The optimisation of the grinding of silicon carbide with diamond wheels using genetic algorithms , 2003 .

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

[8]  S. G. Deshmukh,et al.  A genetic algorithmic approach for optimization of surface roughness prediction model , 2002 .

[9]  P. V. Rao,et al.  Selection of optimum conditions for maximum material removal rate with surface finish and damage as constraints in SiC grinding , 2003 .

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

[11]  Yung C. Shin,et al.  Evolutionary modelling and optimization of grinding processes , 2000 .

[12]  Andrew Y. C. Nee,et al.  Micro-computer-based optimization of the surface grinding process , 1992 .

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

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

[15]  C.-T. Su,et al.  Optimization of machining conditions for turning cylindrical stocks into continuous finished profiles , 1998 .

[16]  R. Saravanan,et al.  Machining Parameters Optimisation for Turning Cylindrical Stock into a Continuous Finished Profile Using Genetic Algorithm (GA) and Simulated Annealing (SA) , 2003 .

[17]  Shiang-Tai Liu,et al.  An economic machining process model with interval parameters , 2007 .

[18]  James Kennedy,et al.  Particle swarm optimization , 1995, Proceedings of ICNN'95 - International Conference on Neural Networks.

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

[20]  Azim Houshyar,et al.  Quality and optimum parameter selection in metal cutting , 1992 .

[21]  J. S. Agapiou.  Optimization of Multistage Machining Systems, Part 1: Mathematical Solution , 1992 .

[22]  T. S. Lee,et al.  A particle swarm approach for grinding process optimization analysis , 2007 .

[23]  M. S. Shunmugam,et al.  Optimal sub-division of the depth of cut to achieve minimum production cost in multi-pass turning using a genetic algorithm , 1998 .

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