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A hybrid approach for robust diagnostics of cutting tools

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2 Author(s)
K. Ramamurthi ; Semicond. Process & Design Center, Texas Instrum. Inc., Dallas, TX, USA ; C. L. Hough

A new multisensor based hybrid technique has been developed for robust diagnosis of cutting tools. The technique combines the concepts of pattern classification and real-time knowledge based systems (RTKBS) and draws upon their strengths; learning facility in the case of pattern classification and a higher level of reasoning in the case of RTKBS. It eliminates some of their major drawbacks: false alarms or delayed/lack of diagnosis in case of pattern classification and tedious knowledge base generation in case of RTKBS. It utilizes a dynamic distance classifier, developed upon a new separability criterion and a new definition of robust diagnosis for achieving these benefits. The promise of this technique has been proven concretely through an on-line diagnosis of drill wear. Its suitability for practical implementation is substantiated by the use of practical, inexpensive, machine-mounted sensors and low-cost delivery systems

Published in:

IEEE Transactions on Systems, Man, and Cybernetics  (Volume:24 ,  Issue: 3 )