Tool wear monitoring using artificial neural networks

An on-line scheme for monitoring tool wear in unmanned machining operations using artificial neural networks (ANNs) is proposed. Various configurations of ANNs are studied to increase the accuracy of tool wear estimation. With this aim three configurations of the ANNs namely, an ANN without memory, an ANN with one phase memory, and an ANN with two phase memory are considered. Each ANN is trained to associate an input vector which consists of values of cutting conditions, with an output vector containing flank wear as a single output. The training data and evaluation data is generated using the popular analytical tool wear model. The performance of all the ANNs are compared by considering four different cases of evaluation data. The proposed scheme of tool wear modeling using ANNs is easily extendible to include other cutting parameters and can be implemented in real-time.