Balancing multiple criteria in formulation of weighted, single-objective genetic algorithm optimization for CNC machining problems

Abstract

This paper presents results obtained from the implementation of a genetic algorithm (GA) to a simplified multi-objective machining optimization problem. The major goal is to examine the effect of crucial machining parameters imparted to computer numerical control machining operations when properly balanced conflicting criteria referring to part quality and process productivity are treated as a single optimization objective. Thus the different combinations of weight coefficient values were examined in terms of their significance to the problem's response. Under this concept, a genetic algorithm was applied to optimize the process parameters exist in typical; commercially available CAM systems with significantly low computation cost. The algorithm handles the simplified linear weighted criteria expression as its objective function. It was found that optimization results vary noticeably under the influence of different weighing coefficients. Thus, the obtained optima differentiate, since balancing values strongly affect optimization objective functions.

Surface Qualification Toolpath Optimization for Hybrid Manufacturing

“…The weight values must also add up to 1, which is an Archimedean formulation of the objective function [35]. This method of single objective optimization with multiple criteria was also used by Krimpenis et al [36] in their machining optimization study. The objective function can be seen below, along with the Archimedean weights constraint:…”