Modeling and optimization of cold extrusion process by using response surface methodology and metaheuristic approaches
Obtaining the optimal extrusion process parameters by integration of optimization techniques was crucial and continuous engineering task in which it attempted to minimize the tool load. The tool load should be minimized as higher extrusion forces required greater capacity and energy. It may lead to...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Springer
2018
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| Online Access: | http://eprints.uthm.edu.my/5562/1/AJ%202018%20%28215%29.pdf |
| _version_ | 1796869221027676160 |
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| author | Ong, Pauline Vui, Desmond Daniel Sheng Chin Choon, Sin Ho Chuan, Huat Ng |
| author_facet | Ong, Pauline Vui, Desmond Daniel Sheng Chin Choon, Sin Ho Chuan, Huat Ng |
| author_sort | Ong, Pauline |
| collection | UTHM |
| description | Obtaining the optimal extrusion process parameters by integration of optimization techniques was crucial and continuous engineering task in which it attempted to minimize the tool load. The tool load should be minimized as higher extrusion forces required greater capacity and energy. It may lead to increase the chance of part defects, die wear and die breakage. Besides, optimization may help to save the time and cost of producing the final product, in addition to produce better formability of work material and better quality of the finishing product. In this regard, this study aimed to determine the optimal extrusion process parameters. The minimization of punch load was the main concern, in such a way that the structurally sound product at minimum load can be achieved. Minimization of punch load during the extrusion process was first formulated as a nonlinear programming model using response surface methodology in this study. The established extrusion force model was then taken as the fitness function. Subsequently, the analytical approach and metaheuristic algorithms, specifically the particle swarm optimization, cuckoo search algorithm (CSA) and flower pollination algorithm, were applied to optimize the extrusion process parameters. Performance assessment demonstrated the promising results of all presented techniques in minimizing the tool loading. The CSA, however, gave more persistent optimization results, which was validated through statistical analysis |
| first_indexed | 2024-03-05T21:51:36Z |
| format | Article |
| id | uthm.eprints-5562 |
| institution | Universiti Tun Hussein Onn Malaysia |
| language | English |
| last_indexed | 2024-03-05T21:51:36Z |
| publishDate | 2018 |
| publisher | Springer |
| record_format | dspace |
| spelling | uthm.eprints-55622022-01-17T01:34:52Z http://eprints.uthm.edu.my/5562/ Modeling and optimization of cold extrusion process by using response surface methodology and metaheuristic approaches Ong, Pauline Vui, Desmond Daniel Sheng Chin Choon, Sin Ho Chuan, Huat Ng TJ Mechanical engineering and machinery TS200-770 Metal manufactures. Metalworking Obtaining the optimal extrusion process parameters by integration of optimization techniques was crucial and continuous engineering task in which it attempted to minimize the tool load. The tool load should be minimized as higher extrusion forces required greater capacity and energy. It may lead to increase the chance of part defects, die wear and die breakage. Besides, optimization may help to save the time and cost of producing the final product, in addition to produce better formability of work material and better quality of the finishing product. In this regard, this study aimed to determine the optimal extrusion process parameters. The minimization of punch load was the main concern, in such a way that the structurally sound product at minimum load can be achieved. Minimization of punch load during the extrusion process was first formulated as a nonlinear programming model using response surface methodology in this study. The established extrusion force model was then taken as the fitness function. Subsequently, the analytical approach and metaheuristic algorithms, specifically the particle swarm optimization, cuckoo search algorithm (CSA) and flower pollination algorithm, were applied to optimize the extrusion process parameters. Performance assessment demonstrated the promising results of all presented techniques in minimizing the tool loading. The CSA, however, gave more persistent optimization results, which was validated through statistical analysis Springer 2018 Article PeerReviewed text en http://eprints.uthm.edu.my/5562/1/AJ%202018%20%28215%29.pdf Ong, Pauline and Vui, Desmond Daniel Sheng Chin and Choon, Sin Ho and Chuan, Huat Ng (2018) Modeling and optimization of cold extrusion process by using response surface methodology and metaheuristic approaches. Neural Computing and Applications, 29. pp. 1077-1087. ISSN 0941-0643 |
| spellingShingle | TJ Mechanical engineering and machinery TS200-770 Metal manufactures. Metalworking Ong, Pauline Vui, Desmond Daniel Sheng Chin Choon, Sin Ho Chuan, Huat Ng Modeling and optimization of cold extrusion process by using response surface methodology and metaheuristic approaches |
| title | Modeling and optimization of cold extrusion process by using response surface methodology and metaheuristic approaches |
| title_full | Modeling and optimization of cold extrusion process by using response surface methodology and metaheuristic approaches |
| title_fullStr | Modeling and optimization of cold extrusion process by using response surface methodology and metaheuristic approaches |
| title_full_unstemmed | Modeling and optimization of cold extrusion process by using response surface methodology and metaheuristic approaches |
| title_short | Modeling and optimization of cold extrusion process by using response surface methodology and metaheuristic approaches |
| title_sort | modeling and optimization of cold extrusion process by using response surface methodology and metaheuristic approaches |
| topic | TJ Mechanical engineering and machinery TS200-770 Metal manufactures. Metalworking |
| url | http://eprints.uthm.edu.my/5562/1/AJ%202018%20%28215%29.pdf |
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