Automatic supervision of blanking tool wear using pattern recognition analysis

Wing Bun Lee, Chi Fai Cheung, W. M. Chiu, L. K. Chan

Research output: Journal article publicationJournal articleAcademic researchpeer-review

24 Citations (Scopus)

Abstract

Most of the blanking operations are now done on automatic high speed presses. The faster operation, closer dimensional and form tolerances, higher precision demand automatic supervision of blanking tool conditions in order to avoid producing a large volume of defective parts unnoticed. In this paper, an architecture of an automatic supervisory system for monitoring blanking punch wear under various die wear conditions is proposed. The system employs an autoregressive (AR) time-series model to predict the on-line captured peak blanking force. The AR model coefficients are updated by a modified least mean square (MLMS) adaptive filter so as to minimize the prediction error. An optimum number of AR model coefficients are selected to form the pattern vector which is classified by a least mean squared error (LMSE) classifier. Classification of the punch wear states is accomplished by a linear discriminant function (LDF). The performance of the system was evaluated through a series of blanking experiments. Experimental results indicated a high success rate for recognizing blanking punch wear under various die wear conditions.
Original languageEnglish
Pages (from-to)1079-1095
Number of pages17
JournalInternational Journal of Machine Tools and Manufacture
Volume37
Issue number8
DOIs
Publication statusPublished - 1 Jan 1997

ASJC Scopus subject areas

  • Mechanical Engineering
  • Industrial and Manufacturing Engineering

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