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| A Comparison of Optimization Methods in Cutting Parameters Using Non-dominated Sorting Genetic Algorithm (NSGA-II) and Micro Genetic Algorithm (MGA)
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Full
text: |
 PDF(303.4KB) |
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Source |
International Journal of Experimental Algorithms (IJEA) |
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Table of Contents |
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Download
Complete Issue PDF(1.06MB) |
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Volume: 2 Issue: 2 |
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Pages: NULL |
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Publication
Date: September / October 2011 |
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ISSN
(Online): 2180-1282 |
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Pages |
62 - 73 |
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Author(s) |
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Published
Date |
05-10-2011 |
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Publisher |
CSC
Journals, Kuala Lumpur,
Malaysia |
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ADDITIONAL
INFORMATION |
| Keywords Abstract References Cited by Related Articles Collaborative
Colleague |
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KEYWORDS: Cutting Parameters, Surface Roughness, Tool life Criteria, Optimizing, NSGA-II, MGA |
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| This Manuscript is indexed in the following databases/websites:- |
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| 1. Docstoc |
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| Since cutting conditions have an influence on reducing the production cost and time and deciding the quality of a final product the determination of optimal cutting parameters such as cutting speed, feed rate, depth of cut and tool geometry is one of vital modules in process planning of metal parts. With use of experimental results and subsequently, with exploitation of main effects plot, importance of each parameter is studied. In this investigation these parameters was considered as input in order to optimized the surface finish and tool life criteria, two conflicting objectives, as the process performance simultaneously. In this study, micro genetic algorithm (MGA) and Non-dominated Sorting Genetic Algorithm (NSGA-II) were compared with each other proving the superiority of Non-dominated Sorting Genetic Algorithm over micro genetic since Non-dominated Sorting Genetic Algorithm results were more satisfactory than micro genetic algorithm in terms of optimizing machining parameters. |
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| Abolfazl Golshan : Colleagues
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| Mostafa Rezazadeh Shidar : Colleagues
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