A particle swarm approach for grinding process optimization analysis
Optimization is necessary for the control of any process to achieve better product quality, high productivity with low cost. The grinding of silicon carbide is not an easy task due to its low fracture toughness, therefore making the material sensitive to cracking. The efficient grinding involves the...
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| Format: | Article |
| Language: | English |
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SPRINGER LONDON LTD
2007
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| Online Access: | http://shdl.mmu.edu.my/3143/ http://shdl.mmu.edu.my/3143/1/1157.pdf |
| _version_ | 1848790245894520832 |
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| author | Lee, T. S. Ting, T. O. Lin, Y. J. Htay, Than |
| author_facet | Lee, T. S. Ting, T. O. Lin, Y. J. Htay, Than |
| author_sort | Lee, T. S. |
| building | MMU Institutional Repository |
| collection | Online Access |
| description | Optimization is necessary for the control of any process to achieve better product quality, high productivity with low cost. The grinding of silicon carbide is not an easy task due to its low fracture toughness, therefore making the material sensitive to cracking. The efficient grinding involves the optimal selection of operating parameters to maximize the material removal rate (MRR) while maintaining the required surface finish and limiting surface damage. In this work, optimization based on the available model has been carried out to obtain optimum parameters for silicon carbide grinding via particle swarm optimization (PSO) based on the objective of maximizing MRR with reference to surface finish and damage. Based on statistical analysis for various constraint values of surface roughness and number of flaws, simulation results obtained for this machining process for PSO are comparatively better to genetic algorithm (GA) approach. In addition, the post-optimal robustness of PSO has also been studied. From simulation results together with the proposed robustness measurement method, it has been shown that PSO is a convergent stable algorithm. |
| first_indexed | 2025-11-14T18:09:33Z |
| format | Article |
| id | mmu-3143 |
| institution | Multimedia University |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-14T18:09:33Z |
| publishDate | 2007 |
| publisher | SPRINGER LONDON LTD |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | mmu-31432014-03-03T04:30:05Z http://shdl.mmu.edu.my/3143/ A particle swarm approach for grinding process optimization analysis Lee, T. S. Ting, T. O. Lin, Y. J. Htay, Than T Technology (General) QA75.5-76.95 Electronic computers. Computer science Optimization is necessary for the control of any process to achieve better product quality, high productivity with low cost. The grinding of silicon carbide is not an easy task due to its low fracture toughness, therefore making the material sensitive to cracking. The efficient grinding involves the optimal selection of operating parameters to maximize the material removal rate (MRR) while maintaining the required surface finish and limiting surface damage. In this work, optimization based on the available model has been carried out to obtain optimum parameters for silicon carbide grinding via particle swarm optimization (PSO) based on the objective of maximizing MRR with reference to surface finish and damage. Based on statistical analysis for various constraint values of surface roughness and number of flaws, simulation results obtained for this machining process for PSO are comparatively better to genetic algorithm (GA) approach. In addition, the post-optimal robustness of PSO has also been studied. From simulation results together with the proposed robustness measurement method, it has been shown that PSO is a convergent stable algorithm. SPRINGER LONDON LTD 2007 Article NonPeerReviewed text en http://shdl.mmu.edu.my/3143/1/1157.pdf Lee, T. S. and Ting, T. O. and Lin, Y. J. and Htay, Than (2007) A particle swarm approach for grinding process optimization analysis. The International Journal of Advanced Manufacturing Technology, 33 (11-12). 1128-1135 . ISSN 0268-3768 http://dx.doi.org/10.1007/s00170-006-0538-y doi:10.1007/s00170-006-0538-y doi:10.1007/s00170-006-0538-y |
| spellingShingle | T Technology (General) QA75.5-76.95 Electronic computers. Computer science Lee, T. S. Ting, T. O. Lin, Y. J. Htay, Than A particle swarm approach for grinding process optimization analysis |
| title | A particle swarm approach for grinding process optimization analysis |
| title_full | A particle swarm approach for grinding process optimization analysis |
| title_fullStr | A particle swarm approach for grinding process optimization analysis |
| title_full_unstemmed | A particle swarm approach for grinding process optimization analysis |
| title_short | A particle swarm approach for grinding process optimization analysis |
| title_sort | particle swarm approach for grinding process optimization analysis |
| topic | T Technology (General) QA75.5-76.95 Electronic computers. Computer science |
| url | http://shdl.mmu.edu.my/3143/ http://shdl.mmu.edu.my/3143/ http://shdl.mmu.edu.my/3143/ http://shdl.mmu.edu.my/3143/1/1157.pdf |