INSTRUCTIONS--page 1

Abstract

This paper presents a new methodology for continual improvement of cutting conditions with GA (Genetic Algorithms). It performs the following: the modification of recommended cutting conditions obtained from a machining data, learning of obtained cutting conditions using neural networks and the substitution of better cutting conditions for those learned previously by a proposed GA. Operators usually select the machining parameters according to handbooks or their experience, and the selected machining parameters are usually conservative to avoid machining failure. Compared to traditional optimisation methods, a GA is robust, global and may be applied generally without recourse to domain-specific heuristics. Experimental results show that the proposed genetic algorithm-based procedure for solving the optimization problem is both effective and efficient, and can be integrated into an intelligent manufacturing system for solving complex machining optimization problem