dor_id: 26045

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650.#.4.x: Ingenierías

336.#.#.b: article

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351.#.#.6: http://www.revistas.unam.mx/index.php/ingenieria/index

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351.#.#.a: Artículos

harvesting_group: RevistasUNAM

270.1.#.p: Revistas UNAM. Dirección General de Publicaciones y Fomento Editorial, UNAM en revistas@unam.mx

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883.#.#.a: Revistas UNAM

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883.#.#.1: https://www.publicaciones.unam.mx/

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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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Artículo

ANN Based Tool Condition Monitoring System for CNC Milling Machines

Mota Valtierra, G. C.; Franco Gasca, L. A.; Herrera Ruiz, G.; Macias Bobadilla, G.

Facultad de Ingeniería, UNAM, publicado en Ingeniería, Investigación y Tecnología, y cosechado de Revistas UNAM

Licencia de uso

Procedencia del contenido

Cita

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

Descripción del recurso

Autor(es)
Mota Valtierra, G. C.; Franco Gasca, L. A.; Herrera Ruiz, G.; Macias Bobadilla, G.
Tipo
Artículo de Investigación
Área del conocimiento
Ingenierías
Título
ANN Based Tool Condition Monitoring System for CNC Milling Machines
Fecha
2011-11-23
Resumen
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.
Tema
Breakage; wear; wavelet transform; artificial neural networks; monitoring system; fir filter; breakage; wear; wavelet transform; artificial neural networks; monitoring system; fir filter
Idioma
spa
ISSN
ISSN impreso: 1405-7743

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