Optimization of multi-pass turning operations using genetic algorithms for the selection of cutting conditions and cutting tools with tool-wear effect

Abstract

A new genetic algorithms-based method is applied for the optimization of cutting conditions and the selection of cutting tools in multi-pass turning operations. A new methodology for the allocation of total depth of cut in multi-pass turning operations is also developed. A comprehensive optimization criterion for multi-pass turning operations is developed and used as the objective function integrating the contributing effects of all major machining performance measures in all passes. The effect of progressive tool wear in optimization processes for multi-pass turning operations is included. Presented case studies demonstrate the application of the new methodology for optimal allocation of total depth of cut as well as optimization of cutting conditions and the selection of cutting tool inserts, and offer a comparison between optimization processes with and without the effect of tool wear in all passes.