Este artículo presenta los resultados de un estudio comparativo de distintas configuraciones de redes neuronales aplicadas al proceso de corte por electroerosión por hilo (WEDM). El objetivo perseguido es detectar con antelación comportamientos de corte degradado que alertan del riesgo creciente de la rotura de la herramienta empleada en este proceso de mecanizado: el hilo. Cuando esto sucede, disminuye la productividad de manera significativa. Así, partiendo de un trabajo previo en el que se identificaron diferentes tipos de comportamientos degradados, se ha realizado un estudio comparativo contemplando distintos criterios. Entre ellos, destaca la comparación de arquitecturas clásicas de red y, más concretamente, la arquitectura estática Perceptrón Multicapa, y la arquitectura recurrente Elman. La conclusión del trabajo ha sido que la arquitectura Elman constituye la alternativa más adecuada para la detección de la degradación del proceso.
Información de la revista
Vol. 6. Núm. 1.
Páginas 39-50 (enero 2009)
Vol. 6. Núm. 1.
Páginas 39-50 (enero 2009)
Open Access
Aplicación de Redes Neuronales en la Detección de Regímenes Degradados en el Proceso Wedm
Visitas
3256
E. Portillo, I. Cabanes, M. Marcos, A. Zubizarreta
Departamento de Ingeniería de Sistemas y Automática, Universidad del País Vasco, C/ Alameda Urquijo s/n, 48013, Bilbao, España
Este artículo ha recibido
Información del artículo
Resumen
Palabras Clave:
WEDM
electroerosión
RNA
redes neuronales artificiales
Perceptrón Multicapa
Elman
El Texto completo está disponible en PDF
Referencias
[Behrens and Ginzel, 2003]
A. Behrens, J. Ginzel.
Neuro-Fuzzy Process Control System for Sinking EDM.
Journal of Manufacturing processes, Vol. 5 (2003), pp. 33-39
[Cabanes et al., 2008a]
I. Cabanes, E. Portillo, M. Marcos, J.A. Sánchez.
Online prevention of wire breakage in wire electro-discharge machining.
Robotics and Computer-Integrated Manufacturing, Vol 24 (2008), pp. 287-298
[Cabanes et al., 2008b]
I. Cabanes, E. Portillo, M. Marcos, J.A. Sánchez.
Industrial application for on-line detection of instability and wire breakage in wire EDM).
Journal of Materials Processing Technology, Vol. 195 (2008), pp. 101-109
[Elman, 1990]
J.L. Elman.
Finding Structure in Time.
Cognitive Science, Vol. 14 (1990), pp. 179-211
[Fenggou and Dayong, 2004]
C. Fenggou, Y. Dayong.
The study of high efficiency and intelligent optimization system in EDM sinking process.
Journal of Materials Processing Technology, Vol. 149 (2004), pp. 83-87
[Ho et al., 2004]
K.H. Ho, S.T. Newman, S. Rahimifard, R.D. Allen.
State of the art in wire electrical discharge machining (WEDM).
International Journal of Machine Tools and Manufacture, Vol. 44 (2004), pp. 1247-1259
[Huang, 2000]
J.T. Huang, Y.S. Liao.
A wire edm maintenance and fault-diagnosis expert system integrated with an artificial neural network.
Int. J. Prod. Res, Vol. 38 (2000), pp. 1071-1082
[Isasi Viñuela and Galván León, 2004]
P. Isasi Viñuela, I.M. Galván León.
Redes de Neuronas Artificiales. Un enfoque práctico.
Ed. Pearson Educación, S.A, (2004),
[Kao, 1997]
J.Y. Kao, Y.S. Tarng.
A neural-network approach for the on-line monitoring of the electrical discharge machining process.
Journal of Materials Processing Technology, Vol. 69 (1997), pp. 112-119
[Kunieda et al., 2001]
M. Kunieda, S. Saga, H. Yoshino, T. Ohta, M. Kobayashi.
Control of discharge locations in EDM with locally imposed high electric field.
Proceedings of the XIII ISEM, Vol. 2 (2001), pp. 485-495
[Lauwers et al., 1999]
B. Lauwers, J.P. Kruth, P.H. Bleys, B. Van Coppenolle, L. Stevens, R. Derighetti.
Wire rupture prevention using on-line pulse localisation in WEDMB.
Vdi Berichte, Vol. 1405 (1999), pp. 203-213
[Liao and Woo, 1997a]
Y.S. Liao, J.C. Woo.
The effects of machining settings on the behaviour of pulse trains in the WEDM process.
Journal of Materials Processing Technology, Vol. 71 (1997), pp. 433-439
[Liao et al., 1997b]
Y.S. Liao, Y.Y. Chu, M.T. Yan.
Study of wire breaking process and monitoring of WEDM.
International Journal of Machine Tools and Manufacture, Vol. 37 (1997), pp. 555-567
[Liao and Woo, 2000]
Y.S. Liao, J.C. Woo.
Design of a fuzzy control system for the adaptive control of WEDM process.
International Journal of Machine Tools and Manufacture, 40 (2000), pp. 2293-2307
[Mediliyegedara et al., 2004]
T.K.K.R. Mediliyegedara, A.K.M. De Silva, D.K. Harrison, J.A. McGeough.
An intelligent pulse classification system for electro-chemical discharge, machining (ECDM)—a preliminary study.
Journal of Materials Processing Technology, Vol. 149 (2004), pp. 499-503
[Obara et al., 1999]
H. Obara, M. Abe, T. Ohsumi.
Control of Wire Breakage during Wire EDM.
International Journal of Electrical Machining, (1999),
[Portillo et al., 2007]
E. Portillo, I. Cabanes, M. Marcos, D. Orive, J.A. Sánchez.
Design of a virtual instrumentation system for a machining process.
IEEE Transactions on Instrumentation and Measurement., Vol. 56 (2007), pp. 2616-2622
[Sarle and Neural FAQ, 2002]
Sarle W.S. Neural FAQ (2002) ftp://ftp.sas.com/pub/neural/FAQ.html
[Sarle, 1995]
W.S. Sarle.
Stopped training and other remedies for overfitting.
Proceedings of the 27th Symposium on the Interface of Computing Science and Statistics, (1995), pp. 352-360
[Shoda et al., 1995]
K. Shoda, Y. Kaneko, H. Nishimura, M. Kunieda, M.X. Fan.
Development of adaptive control system to prevent EDM wire breakage.
EDM technology, Vol. 3 (1995), pp. 17-22
[Tarng et al., 1995]
Y.S. Tarng, S.C. Ma, L.K. Chung.
Determination of optimal cutting parameters in WEDM.
International Journal of Machine Tools and Manufacture, Vol. 35 (1995), pp. 1693-1701
[Tetko et al., 1995]
I.V. Tetko, D.J. Livingstone, A.I. Luik.
Neural Network Studies. 1. Comparison of overfitting and overtraining.
Journal of Chemical Information and Computer Science, Vol. 35 (1995), pp. 826-833
[Tiira, 1999]
T. Tiira.
Detecting teleseismic events using artificial neural networks.
Computers & Geosciences, Vol. 25 (1999), pp. 929-938
[Tsai and Wang, 2001]
K. Tsai, P. Wang.
Predictions on surface finish in electrical discharge machining based upon neural network models.
International Journal of Machine Tools & Manufacture, Vol. 41 (2001), pp. 1385-1403
[Valverde and Gachet, 2007]
R. Valverde, D. Gachet.
Identificación de sistemas dinámicos utilizando redes neuronales RBF.
RIAI, Vol. 4 (2007), pp. 32-42
[Wang and G, 2004]
G. Wang.
Two-Phase Reverse Neural Network Approach for Modeling a Complicate Manufacturing Process with Small Sample Size.
Neural Information Processing, Vol. 2 (2004),
[Wu and Li, 2001]
J. Wu, M.H. Li.
The identification of the servo control state in wire electrical discharge machining process.
ISEM XIII, (2001), pp. 423-433
[Yan, 1995]
M.T. Yan, Y.S. Liao.
Adaptive control of WEDM process using the fuzzy control strategy.
ISEM XI, (1995), pp. 343-352
[Yan, 2001]
Liao Chang Yan.
On-line Estimation of Workpiece Height by using Neural Networks and Hierarchical adaptive Control of WEDM.
J. Advanced Manufacturing Technology, Vol.18 (2001), pp. 884-889
Copyright © 2009. Elsevier España, S.L.. Todos los derechos reservados