Pareto-Optimal Solutions for Multi-Objective Optimization of Turning Operation using Nondominated Sorting Genetic Algorithm
Publish Year: 1384
نوع سند: مقاله کنفرانسی
زبان: English
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شناسه ملی سند علمی:
ICME07_056
تاریخ نمایه سازی: 6 آذر 1388
Abstract:
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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Authors
A.A Akbari
Assistant Professor Mechanical Department, Faculty of EngineeringFerdowsi University of Mashhad, Mashhad, Iran
M Tazimi
M. Sc. University Student
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