Tool condition monitoring in metal cutting through application of MLP neural networks

This paper describes preliminary results of the application of feedforward multilayer perceptron (MLP) neural networks for cutting tool state identification in a metal turning operation. Test cuts were conducted using carbide inserts with and without wear on alloy steel and the acquired data used to train and test the generalization capabilities of two MLP configurations. Obtained results for successful classification of the tool state with respect to worn and sharp classes were between 83-96%