0
Technical Briefs

A New Method for Discharge State Prediction of Micro-EDM Using Empirical Mode Decomposition

[+] Author and Article Information
Zhenyuan Jia, Fuji Wang, Wei Liu

School of Mechanical Engineering, Dalian University of Technology, Dalian, Liaoning 116024, China

Lingxuan Zhang1

School of Mechanical Engineering, Dalian University of Technology, Dalian, Liaoning 116024, China

1

Corresponding author.

J. Manuf. Sci. Eng 132(1), 014501 (Dec 22, 2009) (6 pages) doi:10.1115/1.4000559 History: Received November 21, 2008; Revised October 25, 2009; Published December 22, 2009; Online December 22, 2009

The property of high frequency in micro-EDM (electrical discharge machining) causes the discharge states to vary much faster than in conventional EDM, and discharge states of micro-EDM have the characteristics of nonstationarity, nonlinearity, and internal coupling, all of this makes it very difficult to carry out stable control. Thus empirical mode decomposition is adopted to conduct the prediction of the discharge states obtained through multisensor data fusion and fuzzy logic in micro-EDM. Combined with the autoregressive (AR) model identification and linear prediction, the mathematical model for EDM discharge state prediction using empirical mode decomposition is established and the corresponding prediction method is presented. Experiments demonstrate that the new prediction method with short identification data is highly accurate and operates quickly. Even using short model identification data, the accuracy of empirical mode decomposition prediction can stably reach a correlation of 74%, which satisfies statistical expectations. Additionally, the new process can also effectively eliminate the lag of conventional prediction methods to improve the efficiency of micro-EDM, and it provides a good basis to enhance the stability of the control system.

FIGURES IN THIS ARTICLE
<>
Copyright © 2010 by American Society of Mechanical Engineers
Your Session has timed out. Please sign back in to continue.

References

Figures

Grahic Jump Location
Figure 1

Input membership functions for sampling points

Grahic Jump Location
Figure 2

Output membership functions for sampling points

Grahic Jump Location
Figure 3

Gap voltage and gap current signals for sampling points

Grahic Jump Location
Figure 4

Discharge state sequence time-domain waveform

Grahic Jump Location
Figure 5

The corresponding empirical mode decomposition time-domain waveform

Grahic Jump Location
Figure 6

Comparison of the discharge state predictive and actual outputs

Grahic Jump Location
Figure 7

Comparison of the discharge state predictive and actual outputs (using short data)

Tables

Errata

Discussions

Some tools below are only available to our subscribers or users with an online account.

Related Content

Customize your page view by dragging and repositioning the boxes below.

Related Journal Articles
Related eBook Content
Topic Collections

Sorry! You do not have access to this content. For assistance or to subscribe, please contact us:

  • TELEPHONE: 1-800-843-2763 (Toll-free in the USA)
  • EMAIL: asmedigitalcollection@asme.org
Sign In