Pareto-Optimal Solutions for Multi-Objective Optimization of Turning Operation using Nondominated Sorting Genetic Algorithm

摘要

Many machining operation problems are characterized by their multiple performance measures that are often noncommensurable and competing with each other. The presence of multiple objectives in a problem usually gives rise to a set of optimal solutions, largely known as Pareto-optimal solutions. Evolutionary algorithms have been recognized to be well suited for multi-objective optimization because of their capability to evolve a set of nondominated solutions distributed along the Pareto Front. This has led to the development of many evolutionary multi-objective optimization algorithms among which Nondominated Sorting Genetic Algorithm (NSGA and its enhanced version NSGA-II) has been found effective in solving a wide variety of problems. The purpose of this study is to extend this methodology for solution of multi-objective optimization of turning operation under the framework of NSGA-II. Two objective functions, cost and surface roughness, and three machining parameters, feed rate, cutting speed and depth of cut, are considered. Results show that NSGA-II is a suitable method for our problem.