Computational inteligence in optimization of machining operation parameters of ST-37 steel
Optimal selection of cutting parameters is one of the significant issues in achieving high quality machining. In this study, a method for the selection of optimal cutting parameters during lathe operation is presented. The present study focuses on multiple-performance optimization on machining chara...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Trans Tech Publications
2013
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| Online Access: | http://psasir.upm.edu.my/id/eprint/28735/ http://psasir.upm.edu.my/id/eprint/28735/1/Computational%20inteligence%20in%20optimization%20of%20machining%20operation%20parameters%20of%20ST-37%20steel.pdf |
| _version_ | 1848846198643884032 |
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| author | Golshan, Abolfazl Ghodsiyeh, Danial Gohari, Soheil Ayob, Amran Baharudin, B. T. Hang Tuah |
| author_facet | Golshan, Abolfazl Ghodsiyeh, Danial Gohari, Soheil Ayob, Amran Baharudin, B. T. Hang Tuah |
| author_sort | Golshan, Abolfazl |
| building | UPM Institutional Repository |
| collection | Online Access |
| description | Optimal selection of cutting parameters is one of the significant issues in achieving high quality machining. In this study, a method for the selection of optimal cutting parameters during lathe operation is presented. The present study focuses on multiple-performance optimization on machining characteristics of St-37 steel. The cutting parameters used in this experimental study include cutting speed, feed rate, depth of cut and rake angle. Two output parameters, namely, surface roughness and tool life are considered as process performance. A statistical model based on linear polynomial equations is developed to describe different responses. For optimal conditions, the Non-dominated Sorting Genetic Algorithm (NSGA) is employed in achieving appropriate models. The optimization procedure shows that the proposed method has a high performance in problem-solving. |
| first_indexed | 2025-11-15T08:58:54Z |
| format | Article |
| id | upm-28735 |
| institution | Universiti Putra Malaysia |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-15T08:58:54Z |
| publishDate | 2013 |
| publisher | Trans Tech Publications |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | upm-287352016-02-12T02:21:59Z http://psasir.upm.edu.my/id/eprint/28735/ Computational inteligence in optimization of machining operation parameters of ST-37 steel Golshan, Abolfazl Ghodsiyeh, Danial Gohari, Soheil Ayob, Amran Baharudin, B. T. Hang Tuah Optimal selection of cutting parameters is one of the significant issues in achieving high quality machining. In this study, a method for the selection of optimal cutting parameters during lathe operation is presented. The present study focuses on multiple-performance optimization on machining characteristics of St-37 steel. The cutting parameters used in this experimental study include cutting speed, feed rate, depth of cut and rake angle. Two output parameters, namely, surface roughness and tool life are considered as process performance. A statistical model based on linear polynomial equations is developed to describe different responses. For optimal conditions, the Non-dominated Sorting Genetic Algorithm (NSGA) is employed in achieving appropriate models. The optimization procedure shows that the proposed method has a high performance in problem-solving. Trans Tech Publications 2013 Article PeerReviewed application/pdf en http://psasir.upm.edu.my/id/eprint/28735/1/Computational%20inteligence%20in%20optimization%20of%20machining%20operation%20parameters%20of%20ST-37%20steel.pdf Golshan, Abolfazl and Ghodsiyeh, Danial and Gohari, Soheil and Ayob, Amran and Baharudin, B. T. Hang Tuah (2013) Computational inteligence in optimization of machining operation parameters of ST-37 steel. Applied Mechanics and Materials, 248. pp. 456-461. ISSN 1660-9336; ESSN: 1662-7482 http://www.scientific.net/AMM.248.456 10.4028/www.scientific.net/AMM.248.456 |
| spellingShingle | Golshan, Abolfazl Ghodsiyeh, Danial Gohari, Soheil Ayob, Amran Baharudin, B. T. Hang Tuah Computational inteligence in optimization of machining operation parameters of ST-37 steel |
| title | Computational inteligence in optimization of machining operation parameters of ST-37 steel |
| title_full | Computational inteligence in optimization of machining operation parameters of ST-37 steel |
| title_fullStr | Computational inteligence in optimization of machining operation parameters of ST-37 steel |
| title_full_unstemmed | Computational inteligence in optimization of machining operation parameters of ST-37 steel |
| title_short | Computational inteligence in optimization of machining operation parameters of ST-37 steel |
| title_sort | computational inteligence in optimization of machining operation parameters of st-37 steel |
| url | http://psasir.upm.edu.my/id/eprint/28735/ http://psasir.upm.edu.my/id/eprint/28735/ http://psasir.upm.edu.my/id/eprint/28735/ http://psasir.upm.edu.my/id/eprint/28735/1/Computational%20inteligence%20in%20optimization%20of%20machining%20operation%20parameters%20of%20ST-37%20steel.pdf |