Title :
Monitoring the wear of cutting tools in CNC-lathes with artificial neural networks
Author_Institution :
Fakultat fur Math. und Comput. Sci., Passau Univ., Germany
Abstract :
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
Keywords :
computerised numerical control; cutting; learning (artificial intelligence); machine tools; multilayer perceptrons; parameter estimation; sensor fusion; CNC lathes; artificial neural networks; automatic tool monitoring systems; characteristic coefficients; cutting tools; economic advantages; finish turning; multi-sensor system; multilayer perceptrons; process-specific parameters; rough turning; time-delay neural networks; tool holder; wear monitoring; Artificial neural networks; Computerized monitoring; Cutting tools; Feeds; Multi-layer neural network; Multilayer perceptrons; Neural networks; Parameter estimation; Signal processing; Turning;
Conference_Titel :
Acoustics, Speech, and Signal Processing, 1997. ICASSP-97., 1997 IEEE International Conference on
Conference_Location :
Munich
Print_ISBN :
0-8186-7919-0
DOI :
10.1109/ICASSP.1997.595519
