Global cutting-path optimization considering the minimum heat effect with microgenetic algorithms

Abstract

This article considers microgenetic algorithms (μGAs), which explore in a small population with a few genetic operators, for cutting-path optimization problems. The major difference between μGAs and simple genetic algorithms (SGAs) is how to make a reproductive plan for an improved searching technique because of population choice. It is shown that μGAs implementation reaches the near-optimal region much earlier than the SGAs approach, and the μGAs give a better solution than simulated annealing (SA). The main objective was to determine what temperature distribution can be obtained from the solution of a travelling distributed heat source. The solution of the travelling heat source on nested raw plate provides information about the vertices of each nested part of the raw plate. From the fact that the initial temperature at a piercing point strongly depends on the heat flow which stems from the previous cutting contour, the temperature of all piercing points must be lower than the critical temperature after each cutting of the components of a part. The critical temperature is identified as the mechanical melting temperature of steels. A heuristic back-tracking method is introduced to find the near-optimum cutting path considering the minimum heat effect on deformation. The heuristic back-tracking method is incorporated with the μGAs.