dor_id: 45606

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336.#.#.3: Artículo de Investigación

336.#.#.a: Artículo

351.#.#.6: https://jart.icat.unam.mx/index.php/jart

351.#.#.b: Journal of Applied Research and Technology

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856.4.0.u: https://jart.icat.unam.mx/index.php/jart/article/view/332/329

100.1.#.a: Belloufi, A.; Assas, M.; Rezgui, I.

524.#.#.a: Belloufi, A., et al. (2013). Optimization of Turning Operations by Using a Hybrid Genetic Algorithm with Sequential Quadratic Programming. Journal of Applied Research and Technology; Vol. 11 Núm. 1. Recuperado de https://repositorio.unam.mx/contenidos/45606

245.1.0.a: Optimization of Turning Operations by Using a Hybrid Genetic Algorithm with Sequential Quadratic Programming

502.#.#.c: Universidad Nacional Autónoma de México

561.1.#.a: Instituto de Ciencias Aplicadas y Tecnología, UNAM

264.#.0.c: 2013

264.#.1.c: 2013-02-01

653.#.#.a: multipass turning; genetic algorithm; sequential quadratic programming; optimization of cutting conditions

506.1.#.a: La titularidad de los derechos patrimoniales de esta obra pertenece a las instituciones editoras. Su uso se rige por una licencia Creative Commons BY-NC-SA 4.0 Internacional, https://creativecommons.org/licenses/by-nc-sa/4.0/legalcode.es, para un uso diferente consultar al responsable jurídico del repositorio por medio del correo electrónico gabriel.ascanio@icat.unam.mx

884.#.#.k: https://jart.icat.unam.mx/index.php/jart/article/view/332

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041.#.7.h: eng

520.3.#.a: The determination of optimal cutting parameters is one of the most important elements in any process planning ofmetal parts. In this paper, a new hybrid genetic algorithm by using sequential quadratic programming is used for theoptimization of cutting conditions. It is used for the resolution of a multipass turning optimization case by minimizingthe production cost under a set of machining constraints. The genetic algorithm (GA) is the main optimizer of thisalgorithm whereas SQP Is used to fine tune the results obtained from the GA. Furthermore, the convergencecharacteristics and robustness of the proposed method have been explored through comparisons with resultsreported in literature. The obtained results indicate that the proposed hybrid genetic algorithm by using a sequentialquadratic programming is effective compared to other techniques carried out by different researchers.

773.1.#.t: Journal of Applied Research and Technology; Vol. 11 Núm. 1

773.1.#.o: https://jart.icat.unam.mx/index.php/jart

022.#.#.a: ISSN electrónico: 2448-6736; ISSN: 1665-6423

310.#.#.a: Bimestral

264.#.1.b: Instituto de Ciencias Aplicadas y Tecnología, UNAM

doi: https://doi.org/10.1016/S1665-6423(13)71517-7

harvesting_date: 2023-11-08 13:10:00.0

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last_modified: 2024-03-19 14:00:00

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Artículo

Optimization of Turning Operations by Using a Hybrid Genetic Algorithm with Sequential Quadratic Programming

Belloufi, A.; Assas, M.; Rezgui, I.

Instituto de Ciencias Aplicadas y Tecnología, UNAM, publicado en Journal of Applied Research and Technology, y cosechado de Revistas UNAM

Licencia de uso

Procedencia del contenido

Cita

Belloufi, A., et al. (2013). Optimization of Turning Operations by Using a Hybrid Genetic Algorithm with Sequential Quadratic Programming. Journal of Applied Research and Technology; Vol. 11 Núm. 1. Recuperado de https://repositorio.unam.mx/contenidos/45606

Descripción del recurso

Autor(es)
Belloufi, A.; Assas, M.; Rezgui, I.
Tipo
Artículo de Investigación
Área del conocimiento
Ingenierías
Título
Optimization of Turning Operations by Using a Hybrid Genetic Algorithm with Sequential Quadratic Programming
Fecha
2013-02-01
Resumen
The determination of optimal cutting parameters is one of the most important elements in any process planning ofmetal parts. In this paper, a new hybrid genetic algorithm by using sequential quadratic programming is used for theoptimization of cutting conditions. It is used for the resolution of a multipass turning optimization case by minimizingthe production cost under a set of machining constraints. The genetic algorithm (GA) is the main optimizer of thisalgorithm whereas SQP Is used to fine tune the results obtained from the GA. Furthermore, the convergencecharacteristics and robustness of the proposed method have been explored through comparisons with resultsreported in literature. The obtained results indicate that the proposed hybrid genetic algorithm by using a sequentialquadratic programming is effective compared to other techniques carried out by different researchers.
Tema
multipass turning; genetic algorithm; sequential quadratic programming; optimization of cutting conditions
Idioma
eng
ISSN
ISSN electrónico: 2448-6736; ISSN: 1665-6423

Enlaces