Multi-objective particle swarm algorithm for the posterior selection of machining parameters in multi-pass turning
The approach presented in this paper addresses the machining process optimization problem through realistic modeling of multi-pass operations. It is designed to determine, at the same time, the number of passes and the cutting conditions of each. This is a multi-objective optimization model that sim...
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| Format: | Article |
| Language: | English |
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Elsevier
2021-05-01
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| Series: | Journal of King Saud University: Engineering Sciences |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S1018363920302415 |
| _version_ | 1818580051675840512 |
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| author | Toufik Ameur |
| author_facet | Toufik Ameur |
| author_sort | Toufik Ameur |
| collection | DOAJ |
| description | The approach presented in this paper addresses the machining process optimization problem through realistic modeling of multi-pass operations. It is designed to determine, at the same time, the number of passes and the cutting conditions of each. This is a multi-objective optimization model that simultaneously minimizes the production rate and the used tool life under all technological and organizational constraints based on fundamental cutting laws. The posterior selection of a solution is made from a Pareto front generated by a multi-objective particle swarm algorithm based on the concept of dynamic neighborhood. In an example application which consists in determining the cutting conditions for a turning operation, using this approach has provided a rich set of Pareto optimal solutions that represents all possible compromises. This set offers, normally, all the information needed for the optimal selection of cutting conditions. Despite the complexity of treated problem, the analysis of the obtained results demonstrates the effectiveness of the developed approach. Thus, it presents the possibility of using this approach for other problems from industry. |
| first_indexed | 2024-12-16T07:11:27Z |
| format | Article |
| id | doaj.art-aa6b8dcaf9974dd4b7f05da5172da00a |
| institution | Directory Open Access Journal |
| issn | 1018-3639 |
| language | English |
| last_indexed | 2024-12-16T07:11:27Z |
| publishDate | 2021-05-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Journal of King Saud University: Engineering Sciences |
| spelling | doaj.art-aa6b8dcaf9974dd4b7f05da5172da00a2022-12-21T22:39:53ZengElsevierJournal of King Saud University: Engineering Sciences1018-36392021-05-01334259265Multi-objective particle swarm algorithm for the posterior selection of machining parameters in multi-pass turningToufik Ameur0Address: PB 41 El-Alia Nord, Biskra, Algeria.; Mechanical Engineering Department, Faculty of Applied Sciences, Kasdi Marbeh University, Ouargla, AlgeriaThe approach presented in this paper addresses the machining process optimization problem through realistic modeling of multi-pass operations. It is designed to determine, at the same time, the number of passes and the cutting conditions of each. This is a multi-objective optimization model that simultaneously minimizes the production rate and the used tool life under all technological and organizational constraints based on fundamental cutting laws. The posterior selection of a solution is made from a Pareto front generated by a multi-objective particle swarm algorithm based on the concept of dynamic neighborhood. In an example application which consists in determining the cutting conditions for a turning operation, using this approach has provided a rich set of Pareto optimal solutions that represents all possible compromises. This set offers, normally, all the information needed for the optimal selection of cutting conditions. Despite the complexity of treated problem, the analysis of the obtained results demonstrates the effectiveness of the developed approach. Thus, it presents the possibility of using this approach for other problems from industry.http://www.sciencedirect.com/science/article/pii/S1018363920302415Multi-pass turning operationsMulti-objective optimizationPareto methodsParticle swarm algorithmCutting conditions |
| spellingShingle | Toufik Ameur Multi-objective particle swarm algorithm for the posterior selection of machining parameters in multi-pass turning Journal of King Saud University: Engineering Sciences Multi-pass turning operations Multi-objective optimization Pareto methods Particle swarm algorithm Cutting conditions |
| title | Multi-objective particle swarm algorithm for the posterior selection of machining parameters in multi-pass turning |
| title_full | Multi-objective particle swarm algorithm for the posterior selection of machining parameters in multi-pass turning |
| title_fullStr | Multi-objective particle swarm algorithm for the posterior selection of machining parameters in multi-pass turning |
| title_full_unstemmed | Multi-objective particle swarm algorithm for the posterior selection of machining parameters in multi-pass turning |
| title_short | Multi-objective particle swarm algorithm for the posterior selection of machining parameters in multi-pass turning |
| title_sort | multi objective particle swarm algorithm for the posterior selection of machining parameters in multi pass turning |
| topic | Multi-pass turning operations Multi-objective optimization Pareto methods Particle swarm algorithm Cutting conditions |
| url | http://www.sciencedirect.com/science/article/pii/S1018363920302415 |
| work_keys_str_mv | AT toufikameur multiobjectiveparticleswarmalgorithmfortheposteriorselectionofmachiningparametersinmultipassturning |