Detection and classification for faults in drilling process using vibration analysis

In this era of flexible manufacturing systems, increase in demand of automatic and unattended machining process is very high. Thus arise the need for proper online tool condition monitoring methods, in order to minimize error and waste of work-material. In this study, Support Vector Machine (SVM), Artificial Neural Network (ANN) and Bayes classifier are used to develop such a system for automatic drilling operations with the help of vibration signals. The performances of models generated by these classifiers are compared with each other in order to establish the best method. As the vibration signals were acquired under different drilling parameters, this study also tries to understand the events in drilling process that help in ease of fault classification. Three different kinds of wears were studied and later compared to understand the degree or magnitude of effect of wears on the drilling process and signals.