Abstract

A strategy is developed for identifying cutting tool wear on a face mill by automatically recognizing wear patterns in the cutting force signal. The strategy uses a mechanistic model development to predict forces on a lathe under conditions of wear and extends that model to account for the multiple inserts of a face mill. The extended wear model is then verified through experimentation over the life of the inserts. The predicted force signals are employed to train linear discriminant functions to identify the wear state of the process in a manner suitable for on-line application.

Disciplines

Industrial Engineering | Manufacturing

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URL: http://digitalcommons.calpoly.edu/ime_fac/55