EHU ViSens | Research Areas | Application of intelligent control techniques in wire electrical discharge machining. Aplicación de técnicas de control inteligente en el proceso de electroerosión por hilo (UPV05/114)

The Project

Detection of breakage for a thickness of 50
Detection of breakage for a thickness of 80
Detection of breakage for a thickness of 100
ANN Elman recurrent â€“ parallel configuration

Project Publications

Aplicación de redes neuronales artificiales para la detección de anomalías en el proceso de electroerosión por hilo

Publication Data

Portillo, E., Zubizarreta, A., Cabanes, I. Aplicación de redes neuronales artificiales para la detección de anomalías en el proceso de electroerosión por hilo, Inteligencia Artificial y Sistemas Cognitivos. Aplicaciones de IA en el ámbito Industrial., pp. 1-35, UNIDIGITAL_IASAC , 2023. https://unidigitaliasac.unizar.es/ficha/aplicacion-de-redes-neuronales-artificiales-para-la-deteccion-de-anomalias-en-el-proceso-de
Application of Artificial Neural Networks in Wire Electro-discharge Machining (WEDM)

Publication Data

Portillo, E., Zubizarreta, A., Cabanes, I., Marcos, M. Application of Artificial Neural Networks in Wire Electro-discharge Machining (WEDM), Artificial Neural Networks, Cap. 4, pp. 137-184, Nova Science Publishers, Inc, 2010.
Recurrent ANN for monitoring degraded behaviours in a range of workpiece thicknesses

Publication Data

Portillo, E., Marcos, M., Cabanes, I., Zubizarreta, A. Recurrent ANN for monitoring degraded behaviours in a range of workpiece thicknesses, Engineering Applications of Artificial Intelligence, vol. 2, pp. 1270-1283, 2009. JCR Impact Factor: 1.444 (2009). Quartile: Q1. https://doi.org/10.1016/j.engappai.2009.03.009
Aplicación de redes neuronales en la detección de regímenes degradados en el Proceso WEDM

Publication Data

Portillo, E., Cabanes, I., Marcos, M., Zubizarreta, A. Aplicación de redes neuronales en la detección de regímenes degradados en el Proceso WEDM, Revista Iberoamericana de Automática, RIAI, vol. 6, No. 1, pp. 39-50, 2009. JCR Impact Factor: 0.261 (2009). Quartile: Q4. https://doi.org/10.1016/S1697-7912(09)70075-5
Artificial Neural Networks for Detecting Instability Trends in Different Workpiece Thicknesses in a Machining Process

Publication Data

Portillo, E., Marcos, M., Cabanes, I., Zubizarreta, A., Sánchez, J.A. Artificial Neural Networks for Detecting Instability Trends in Different Workpiece Thicknesses in a Machining Process, American Control Conference 2008, Seattle, Washington, USA, 2008. ISBN: 978-1-4244-2079-7. http://dx.doi.org/10.1109/ACC.2008.4586633

see all

Application of intelligent control techniques in wire electrical discharge machining. Aplicación de técnicas de control inteligente en el proceso de electroerosión por hilo (UPV05/114)

Lead Researchers: Itziar Cabanes Axpe - (01/12/2005 - 30/11/2007)

Research Area: Intelligent manufacturing and industry

Funding Body: University of the Basque Country

Objectives

Based on results from previous projects (Empirical modelling of Wire electrical discharge machining to optimise the discharge quality, and Analysis and Diagnosis of discharge quality in wire electrical discharge machining), which show that, although the basic features of the degraded behaviours are the same in both thicknesses analysed, the thresholds met by virtual measures differ. Thus, we propose the definition of one structure that can automatically adapt parameters in a given range of thicknesses, with the aim of avoiding the need to establish a set of heuristic rules for each thickness and also having to repeat the analysis for the target thickness.

Further, we aim to estimate the degree of influence of each of the degraded behaviours in the degrading process. This estimation allows us to implement future strategies depending on the type of degraded situation.

Both objectives (to detect cutting regimes foreseeing the wire breakage with intermediate thickness compared to the those previously studied, and to estimate the degree of influence of each of the degraded behaviours in the degrading process) must be achieved by one structure.

Achievements

Amongst the achievements of this project, we can highlight the following:

Results of a comparative study of different network configurations based on static (PM) and recurrent (Elman) architectures. This study shows that the Elman architecture is the most adequate to meet the proposed objectives.

Obtaining a parallel configuration of neural networks based on the recurrent architecture (Elman), that can detect different types of degraded behaviour in a range of thickness between 50 and 100 mm. Moreover, such a configuration can estimate the degree of influence of the different types of degraded behaviours.

Acknowledgements

This work has been funded by the University of the Basque Country within the project UPV05/114: Application of intelligent control techniques in wire electrical discharge machining.

Software

In order to carry out the example preparation, training and validation phases, we developed different applications using the neural network tool MatLab^TM, Samplechopkper, Triner and NetSim.

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