dor_id: 26045
506.#.#.a: Público
590.#.#.d: Cada artículo es evaluado mediante una revisión ciega única. Los revisores son externos nacionales e internacionales.
510.0.#.a: Consejo Nacional de Ciencia y Tecnología (CONACyT), Sistema Regional de Información en Línea para Revistas Científicas de América Latina, el Caribe, España y Portugal (Latindex), Scientific Electronic Library Online (SciELO), Red de Revistas Científicas de América Latina y El Caribe, España y Portugal (RedALyC), Organización de Estados Iberoamericanos (CREDI), Actualidad Iberoamericana de Chile, Red Iberomericana de Innovación y Conocimiento Científico (REDIB), Science Direct, Directory of Open Acces Journals, Indice de Revistas Latinoamericanas en Ciencias (Periódica), Bibliografía Latinoamericana (Biblat), Índice Internacional de Revistas Actualidad Iberoamericana (CIT)
561.#.#.u: https://www.ingenieria.unam.mx/
650.#.4.x: Ingenierías
336.#.#.b: article
336.#.#.3: Artículo de Investigación
336.#.#.a: Artículo
351.#.#.6: http://www.revistas.unam.mx/index.php/ingenieria/index
351.#.#.b: Ingeniería, Investigación y Tecnología
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: http://www.revistas.unam.mx/front/
883.#.#.a: Revistas UNAM
590.#.#.a: Coordinación de Difusión Cultural, UNAM
883.#.#.1: https://www.publicaciones.unam.mx/
883.#.#.q: Dirección General de Publicaciones y Fomento Editorial, UNAM
850.#.#.a: Universidad Nacional Autónoma de México
856.4.0.u: http://www.revistas.unam.mx/index.php/ingenieria/article/view/27896/25817
100.1.#.a: Mota Valtierra, G. C.; Franco Gasca, L. A.; Herrera Ruiz, G.; Macias Bobadilla, G.
524.#.#.a: Mota Valtierra, G. C., et al. (2011). ANN Based Tool Condition Monitoring System for CNC Milling Machines. Ingeniería Investigación y Tecnología; Vol 12, No 4, 2011. Recuperado de https://repositorio.unam.mx/contenidos/26045
245.1.0.a: ANN Based Tool Condition Monitoring System for CNC Milling Machines
502.#.#.c: Universidad Nacional Autónoma de México
561.1.#.a: Facultad de Ingeniería, UNAM
264.#.0.c: 2011
264.#.1.c: 2011-11-23
653.#.#.a: Breakage; wear; wavelet transform; artificial neural networks; monitoring system; fir filter; breakage; wear; wavelet transform; artificial neural networks; monitoring system; fir filter
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-ND 4.0 Internacional, https://creativecommons.org/licenses/by-nc-nd/4.0/legalcode.es, fecha de asignación de la licencia 2011-11-23, para un uso diferente consultar al responsable jurídico del repositorio por medio del correo electrónico marciaglez@dirfing.unam.mx
884.#.#.k: http://www.revistas.unam.mx/index.php/ingenieria/article/view/27896
001.#.#.#: oai:ojs.phoenicis.tic.unam.mx:article/27896
041.#.7.h: spa
520.3.#.a: Most of the companies have as objective to manufacture high-quality products, then by optimizing costs, reducing and controlling the variations in its production processes it is possible. within manufacturing industries a very important issue is the tool condition monitoring, since the tool state will determine the quality of products. besides, a good monitoring system will protect the machinery from severe damages. for determining the state of the cutting tools in a milling machine, there is a great variety of models in the industrial market, however these systems are not available to all companies because of their high costs and the requirements of modifying the machining tool in order to attach the system sensors. This paper presents an intelligent classification system which determines the status of cutt ers in a computer numerical control (cnc) milling machine. This tool state is mainly detected through the analysis of the cutting forces drawn from the spindle motors currents. This monitoring system does not need sensors so it is no necessary to modify the machine. The correct classification is made by advanced digital signal processing techniques. just after acquiring a signal, a fir digital filter is applied to the data to eliminate the undesired noisy components and to extract the embedded force components. A wavelet transformation is applied to the filtered signal in order to compress the data amount and to optimize the classifier structure. then a multilayer perceptron- type neural network is responsible for carrying out the classification of the signal. achieving a reliability of 95%, the system is capable of detecting breakage and a worn cutter. most of the companies have as objective to manufacture high-quality products, then by optimizing costs, reducing and controlling the variations in its production processes it is possible. within manufacturing industries a very important issue is the tool condition monitoring, since the tool state will determine the quality of products. besides, a good monitoring system will protect the machinery from severe damages. for determining the state of the cutting tools in a milling machine, there is a great variety of models in the industrial market, however these systems are not available to all companies because of their high costs and the requirements of modifying the machining tool in order to attach the system sensors. This paper presents an intelligent classification system which determines the status of cutt ers in a computer numerical control (cnc) milling machine. This tool state is mainly detected through the analysis of the cutting forces drawn from the spindle motors currents. This monitoring system does not need sensors so it is no necessary to modify the machine. The correct classification is made by advanced digital signal processing techniques. just after acquiring a signal, a fir digital filter is applied to the data to eliminate the undesired noisy components and to extract the embedded force components. A wavelet transformation is applied to the filtered signal in order to compress the data amount and to optimize the classifier structure. then a multilayer perceptron- type neural network is responsible for carrying out the classification of the signal. achieving a reliability of 95%, the system is capable of detecting breakage and a worn cutter.
773.1.#.t: Ingeniería Investigación y Tecnología; Vol 12, No 4 (2011)
773.1.#.o: http://www.revistas.unam.mx/index.php/ingenieria/index
046.#.#.j: 2021-08-03 00:00:00.000000
022.#.#.a: ISSN impreso: 1405-7743
310.#.#.a: Trimestral
264.#.1.b: Facultad de Ingeniería, UNAM
758.#.#.1: http://www.revistas.unam.mx/index.php/ingenieria/index
handle: 559f3ce1e6fe2f62
harvesting_date: 2019-02-06 00:00:00.0
856.#.0.q: application/pdf
245.1.0.b: ANN Based Tool Condition Monitoring System for CNC Milling Machines
last_modified: 2021-08-12 16:00:00
license_url: https://creativecommons.org/licenses/by-nc-nd/4.0/legalcode.es
license_type: by-nc-nd
_deleted_conflicts: 2-64a13ff2ec2a5bb476edddb06a747c48
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