Monitoring the wear of cutting tools in CNC-lathes with artificial neural networks

نویسنده

  • Bernhard Sick
چکیده

One of the most important tasks of automatic tool monitoring systems for CNC-lathes is the supervision of a tool's wear. Considering the state of wear and the actual working process (e.g. rough or nish turning) it is possible to exchange a tool (or only the insert) just in time, which o ers signi cant economic advantages. This paper presents a new method to estimate two wear parameters by means of arti cial neural networks (multilayer perceptrons or time-delay neural networks). The input parameters of the networks are process-speci c parameters (like the feed rate or the depth of cut) and characteristic coe cients extracted from signals measured with a multi-sensor system in the tool holder.

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تاریخ انتشار 1997