Estimation of optimal machining control parameters using artificial bee colony
Modern machining processes such as abrasive waterjet (AWJ) are widely used in manufacturing industries nowadays. Optimizing the machining control parameters are essential in order to provide a better quality and economics machining. It was reported by previous researches that artificial bee colony (...
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| Format: | Article |
| Language: | English |
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Springer Science+Business Media
2013
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| Online Access: | http://ir.unimas.my/id/eprint/46/ http://ir.unimas.my/id/eprint/46/1/estimation%20of%20optional%20machining%20control%20%28abstract%29.pdf |
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| author | Norfadzlan, Yusup Arezoo, Sarkheyli Azlan, Mohd Zain Siti Zaiton, Mohd Hashim Norafida, Ithnin |
| author_facet | Norfadzlan, Yusup Arezoo, Sarkheyli Azlan, Mohd Zain Siti Zaiton, Mohd Hashim Norafida, Ithnin |
| author_sort | Norfadzlan, Yusup |
| building | UNIMAS Institutional Repository |
| collection | Online Access |
| description | Modern machining processes such as abrasive waterjet (AWJ) are widely used in manufacturing industries nowadays. Optimizing the machining control parameters are essential in order to provide a better quality and economics machining. It was reported by previous researches that artificial bee colony (ABC) algorithm has less computation time requirement and offered optimal solution due to its excellent global and local search capability compared to the other optimization soft computing techniques. This research employed ABC algorithm to optimize the machining control parameters that lead to a minimum surface roughness (R a) value for AWJ machining. Five machining control parameters that are optimized using ABC algorithm include traverse speed (V), waterjet pressure (P), standoff distance (h), abrasive grit size (d) and abrasive flow rate (m). From the experimental results, the performance of ABC was much superior where the estimated minimum R a value was 28, 42, 45, 2 and 0.9 % lower compared to actual machining, regression, artificial neural network (ANN), genetic algorithm (GA) and simulated annealing (SA) respectively. |
| first_indexed | 2025-11-15T05:52:35Z |
| format | Article |
| id | unimas-46 |
| institution | Universiti Malaysia Sarawak |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-15T05:52:35Z |
| publishDate | 2013 |
| publisher | Springer Science+Business Media |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | unimas-462016-12-27T03:01:49Z http://ir.unimas.my/id/eprint/46/ Estimation of optimal machining control parameters using artificial bee colony Norfadzlan, Yusup Arezoo, Sarkheyli Azlan, Mohd Zain Siti Zaiton, Mohd Hashim Norafida, Ithnin Q Science (General) T Technology (General) TJ Mechanical engineering and machinery Modern machining processes such as abrasive waterjet (AWJ) are widely used in manufacturing industries nowadays. Optimizing the machining control parameters are essential in order to provide a better quality and economics machining. It was reported by previous researches that artificial bee colony (ABC) algorithm has less computation time requirement and offered optimal solution due to its excellent global and local search capability compared to the other optimization soft computing techniques. This research employed ABC algorithm to optimize the machining control parameters that lead to a minimum surface roughness (R a) value for AWJ machining. Five machining control parameters that are optimized using ABC algorithm include traverse speed (V), waterjet pressure (P), standoff distance (h), abrasive grit size (d) and abrasive flow rate (m). From the experimental results, the performance of ABC was much superior where the estimated minimum R a value was 28, 42, 45, 2 and 0.9 % lower compared to actual machining, regression, artificial neural network (ANN), genetic algorithm (GA) and simulated annealing (SA) respectively. Springer Science+Business Media 2013 Article PeerReviewed text en http://ir.unimas.my/id/eprint/46/1/estimation%20of%20optional%20machining%20control%20%28abstract%29.pdf Norfadzlan, Yusup and Arezoo, Sarkheyli and Azlan, Mohd Zain and Siti Zaiton, Mohd Hashim and Norafida, Ithnin (2013) Estimation of optimal machining control parameters using artificial bee colony. Journal of Intelligent Manufacturing, 25 (6). pp. 1463-1472. ISSN 1572-8145 http://link.springer.com/article/10.1007%2Fs10845-013-0753-y#p 10.1007/s10845-013-0753-y |
| spellingShingle | Q Science (General) T Technology (General) TJ Mechanical engineering and machinery Norfadzlan, Yusup Arezoo, Sarkheyli Azlan, Mohd Zain Siti Zaiton, Mohd Hashim Norafida, Ithnin Estimation of optimal machining control parameters using artificial bee colony |
| title | Estimation of optimal machining control parameters using artificial bee colony |
| title_full | Estimation of optimal machining control parameters using artificial bee colony |
| title_fullStr | Estimation of optimal machining control parameters using artificial bee colony |
| title_full_unstemmed | Estimation of optimal machining control parameters using artificial bee colony |
| title_short | Estimation of optimal machining control parameters using artificial bee colony |
| title_sort | estimation of optimal machining control parameters using artificial bee colony |
| topic | Q Science (General) T Technology (General) TJ Mechanical engineering and machinery |
| url | http://ir.unimas.my/id/eprint/46/ http://ir.unimas.my/id/eprint/46/ http://ir.unimas.my/id/eprint/46/ http://ir.unimas.my/id/eprint/46/1/estimation%20of%20optional%20machining%20control%20%28abstract%29.pdf |