Tool wear monitoring using artificial neural network based on extended Kalman filter weight updation with transformed input patterns

The condition of the tool in a turning operation is monitored by using artificial neural network (ANN). The recursive Kalman filter algorithm is used for weight updation of the ANN. To monitor the status of the tool, tool wear patterns are collected. The patterns are transformed from n-dimensional feature space to a lower dimensional space (two dimensions). This is done by using two discriminant vectors $${\varphi_{1 }}$$ and $${\varphi_{2}}$$. These discriminant vectors are found by optimal discriminant plane method. Thirty patterns are used for training the ANN. A comparison between the classification performances of the ANN trained without reducing the dimensions of the input patterns and with reduced dimensions of the input patterns is done. The ANN trained with transformed tool wear patterns gives better results in terms of improved classification performance in less iteration, when compared with the results of the ANN trained without transforming the dimensions of the input patterns to a lower dimension.