MACHINING CONDITION OPTIMIZATION BY GENETIC ALGORITHMS AND SIMULATED ANNEALING

Optimal machining conditions are the key to economical machining opera tions. In this work, some benchmark machining models are evaluated for optimal machining conditions. These machining models are complex beca use of non-linearities and non-convexity. In this research, we have us ed Genetic Algorithms and Simulated Annealing as optimization methods for solving the benchmark models. An extension of the Simulated Anneal ing algorithm, Continuous Simulated Annealing is also used. The result s are evaluated and compared with each other as well as with previousl y published results which used gradient based methods, such as, SUMT ( Sequential Unconstrained Minimization Technique), Box's Complex Search , Hill Algorithm (Sequential search technique), GRG (Generalized Reduc ed Gradient), etc. We conclude that Genetic Algorithms, Simulated Anne aling and the Continuous Simulated Annealing which are non-gradient ba sed optimization techniques are reliable and accurate for solving mach ining optimization problems and offer certain advantages over gradient based methods. (C) 1997 Elsevier Science Ltd.