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

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 finish turning) it is possible to exchange a tool (or only the insert) just in time, which offers significant economic advantages. This paper presents a new method to estimate two wear parameters by means of artificial neural networks (multilayer perceptrons or time-delay neural networks). The input parameters of the networks are process-specific parameters (like the feed rate or the depth of cut) and characteristic coefficients extracted from signals measured with a multi-sensor system in the tool holder