dor_id: 4149021
506.#.#.a: Público
590.#.#.d: Los artículos enviados a la revista "Journal of Applied Research and Technology", se juzgan por medio de un proceso de revisión por pares
510.0.#.a: Scopus, Directory of Open Access Journals (DOAJ); Sistema Regional de Información en Línea para Revistas Científicas de América Latina, el Caribe, España y Portugal (Latindex); Indice de Revistas Latinoamericanas en Ciencias (Periódica); La Red de Revistas Científicas de América Latina y el Caribe, España y Portugal (Redalyc); Consejo Nacional de Ciencia y Tecnología (CONACyT); Google Scholar Citation
561.#.#.u: https://www.icat.unam.mx/
650.#.4.x: Ingenierías
336.#.#.b: article
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
351.#.#.a: Artículos
harvesting_group: RevistasUNAM
270.1.#.p: Revistas UNAM. Dirección General de Publicaciones y Fomento Editorial, UNAM en revistas@unam.mx
590.#.#.c: Open Journal Systems (OJS)
270.#.#.d: MX
270.1.#.d: México
590.#.#.b: Concentrador
883.#.#.u: https://revistas.unam.mx/catalogo/
883.#.#.a: Revistas UNAM
590.#.#.a: Coordinación de Difusión Cultural
883.#.#.1: https://www.publicaciones.unam.mx/
883.#.#.q: Dirección General de Publicaciones y Fomento Editorial
850.#.#.a: Universidad Nacional Autónoma de México
856.4.0.u: https://jart.icat.unam.mx/index.php/jart/article/view/1818/1010
100.1.#.a: Abdelkrim, Farid; Abdelkrim, Mourad; Belloufi, Abderrahim; Tampu, Catalin; Bogdan, Chiri; Gheorghe, Brabie
524.#.#.a: Abdelkrim, Farid, et al. (2023). Multi-input fuzzy inference system based model to predict the cutting temperature when milling AISI 1060 steel. Journal of Applied Research and Technology; Vol. 21 Núm. 3, 2023; 496-513. Recuperado de https://repositorio.unam.mx/contenidos/4149021
245.1.0.a: Multi-input fuzzy inference system based model to predict the cutting temperature when milling AISI 1060 steel
502.#.#.c: Universidad Nacional Autónoma de México
561.1.#.a: Instituto de Ciencias Aplicadas y Tecnología, UNAM
264.#.0.c: 2023
264.#.1.c: 2023-06-29
653.#.#.a: Fuzzy logic; fuzzy inference system; modeling; Milling; Cutting temperature; Infrared camera; Cutting parameters
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/1818
001.#.#.#: 074.oai:ojs2.localhost:article/1818
041.#.7.h: eng
520.3.#.a: The increase in the cutting temperature during milling has harmful effects which negatively affect the technical and economic machining characteristics such as residual stresses, dimensions of machined parts and tools life. The nature of milling operations and the tool geometry make it difficult to predict or measure the temperature during the machining process, which is why great attention has been paid to measurement and prediction methodologies of cutting temperature during milling. In this work, a new intelligent identification technique of the cutting temperature based on the fuzzy set theory has been proposed to replace the strategy based on the operator qualification. This technique uses a fuzzy multiple input inference system to determine the influence of the cutting parameters on the cutting temperature. The fuzzy modeling is based on an experimental database resulting from the non-contactmeasurement of cutting temperature using an infrared camera with an emissivity setting adapted to the material. The results of the fuzzy system show that the fuzzy model is able to specify results providing a very good correlation between the experimental data and those predicted. The average error of the model was approximately 2.242%. The parameters used for the validation of the model were different from the data used for the construction of the fuzzy rules. The results showed that the most important parameter on the cutting temperature is depth of cut. The results obtained in this paper show that the developed model can be applied to predict the cutting temperature with precision during the milling process.
773.1.#.t: Journal of Applied Research and Technology; Vol. 21 Núm. 3 (2023); 496-513
773.1.#.o: https://jart.icat.unam.mx/index.php/jart
022.#.#.a: ISSN electrónico: 2448-6736; ISSN: 1665-6423
310.#.#.a: Bimestral
300.#.#.a: Páginas: 496-513
264.#.1.b: Instituto de Ciencias Aplicadas y Tecnología, UNAM
doi: https://doi.org/10.22201/icat.24486736e.2023.21.3.1818
harvesting_date: 2023-11-08 13:10:00.0
856.#.0.q: application/pdf
file_creation_date: 2023-06-29 17:14:15.0
file_modification_date: 2023-06-29 17:14:15.0
file_creator: Yolanda G.G.
file_name: 7e47bf814dca0d7de97b902592be343aba75d3a3fe1d12329a245f0fb21e2d98.pdf
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last_modified: 2024-03-19 14:00:00
license_url: https://creativecommons.org/licenses/by-nc-sa/4.0/legalcode.es
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