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

file_pages_number: 18

file_format_version: application/pdf; version=1.7

file_size: 1254443

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

license_url: https://creativecommons.org/licenses/by-nc-sa/4.0/legalcode.es

license_type: by-nc-sa

No entro en nada

No entro en nada 2