(BQ) In this paper, an easily implemented method is developed to describe the variations of EDM process, represented by gap states. On the basis of a time series of gap states from a machining process, the paper first studied a general descriptive model for EDM process, and then equivalently simplified the model for application; after spectral analysis, preprocessing of data, parameters selection and model validation proposed a well-defined model. Finally, by using this model structure and size, an online time-varied predictive model was developed. Experimental verifications showed that this predictive model can quickly and accurately provide one step ahead predictions with mean error less than 2%. This model makes clear that variations of EDM process represented by gap states can be predicted online with a high precision. | ARTICLE IN PRESS International Journal of Machine Tools & Manufacture 48 (2008) 1668–1677 Contents lists available at ScienceDirect International Journal of Machine Tools & Manufacture journal homepage: A time-varied predictive model for EDM process Ming Zhou a,Ã, Fuzhu Han a, Isago Soichiro b a b State Key Laboratory of Tribology, Department of Precision Instruments & Mechanology, Tsinghua University, Beijing 100084, China Makino Milling Machine Co., Ltd, 243-0308, Japan a r t i c l e in f o a b s t r a c t Article history: Received 26 December 2007 Received in revised form 3 July 2008 Accepted 7 July 2008 Available online 17 July 2008 The classification techniques of discharging pulses in EDM have been proved critical in improving productivity, precision and lowering the cost of products, etc. In this paper, an easily implemented method is developed to describe the variations of EDM process, represented by gap states. On the basis of a time series of gap states from a machining process, the paper first studied a general descriptive model for EDM process, and then equivalently simplified the model for application; after spectral analysis, preprocessing of data, parameters selection and model validation proposed a well-defined model. Finally, by using this model structure and size, an online time-varied predictive model was developed. Experimental verifications showed that this predictive model can quickly and accurately provide one step ahead predictions with mean error less than 2%. This model makes clear that variations of EDM process represented by gap states can be predicted online with a high precision. & 2008 Elsevier Ltd. All rights reserved. Keywords: Predictive model Electrical discharge machining Discrimination of discharging pulses System identification 1. Introduction Electrical discharge machining (EDM) process has been a key process for the manufacturing industry, especially for the production of punches and .
