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Production of high-quality products with lower cost and shorter time is an important challenge to face of increasing global competition. Determination of optimal cutting parameters is one of the most important elements in any planning process of metal parts. In this study we present a multi-optimization technique based on genetic algorithms and dynamic programming, to search for optimal cuttings parameters such as cutting depth, feed rate and cutting speed of multi-pass turning processes. Two conflicting objectives, the production cost and operation time are simultaneously optimize under a set of practical of machining constraints. The proposed model deals with multi-pass turning processes in which the cutting operations are divided into multi-pass rough machining and finish machining. Results obtained from Genetic algorithms method are used to define the optimum number of machining passes by dynamic programming; such technique helps us in the decision making process. An example is presented to develop the procedure of this technique.

Multi Pass Optimization of Cutting Conditions by Using the Genetic Algorithms