A Comparative Study on Optimizing CNC Milling Operation Using Simulated Annealing and Genetic Algorithm

Abstract

In this paper, a component from automobile industry is considered for optimizing the end milling operation . The objective function is to minimize the total production cost subject to machine constraints such as cutting power, cutting force, tool life, surface finish of the product and the range of the operating parameters. For solving the above problem, optimization procedures were developed using Simulated Annealing (SA) and Genetic Algorithm (GA). Generally, industries use cutting parameters from the range given by machine/tool suppliers. But it is required to find the optimum point in the given range in order to reduce the cost of production. By implementing the procedures developed in this work, an average of 24.48% reduction in manufacturing cost is indicated. The optimization problem is solved very efficiently using the above procedures. They can be easily modified to suit other machining operations such as turning, cylindrical grinding, surface grinding and nontraditional machining processes.

Keywords: Optimization, Simulated Annealing (SA), Genetic Algorithm (GA), Cutting speed, Feed rate