Application of Genetic Algorithms for Cutting Parameter Optimization

Libao An and Hong Zhang


Genetic Algorithms, Cutting Parameter Optimization, Production Cost, Crossover, Mutation


Genetic Algorithms (GAs) represent a particular class of evolutionary algorithms that make use of techniques motivated by evolutionary biology. They have found applications in mathematics, physics, chemistry, economics, engineering and other fields. This paper addresses multi-pass turning optimization problem for minimum unit production cost. Optimal cutting parameters including cutting speed, feed rate, and depth of cut are solved by a solution procedure using GAs. A machining example is given to illustrate the solution process. The effects of GA operators such as crossover and mutation are studied and the suitable values of crossover and mutation rates for the problem studied in this paper are recommended.

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