This thesis relates to the application of Artificial Intelligence to tool wear monitoring. Themain objective is to develop an intelligent condition monitoring system able to detect when acutting tool is worn out. To accomplish this objective it is proposed to use a combined ExpertSystem and Neural Network able to process data coming from external sensors and combinethis with information from the knowledge base and thereafter estimate the wear state of thetool.The novelty of this work is mainly associatedw ith the configurationo f the proposeds ystem.W iththe combination of sensor-baseidn formation and inferencer ules, the result is an on-line systemthat can learn from experience and can update the knowledge base pertaining to informationassociated with different cutting conditions. Two neural networks resolve the problem ofinterpreting the complex sensor inputs while the Expert System, keeping track of previoussuccesse, stimatesw hich of the two neuraln etworks is more reliable. Also, mis-classificationsa refiltered out through the use of a rough but approximate estimator, the Taylor's tool life equation.In this study an on-line tool wear monitoring system for turning processesh as been developedwhich can reliably estimate the tool wear under common workshop conditions. The system'smodular structurem akesi t easyt o updatea s requiredb y different machinesa nd/or processesT. heuse of Taylor's tool life equation, although weak as a tool life estimator, proved to be crucial inachieving higher performance levels. The application of the Self Organizing Map to tool wearmonitoring is, in itself, new and proved to be slightly more reliable then the Adaptive ResonanceTheory neural network.
展开▼
