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.

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