Automatic tool state identification in a metal turning operation using MLP neural networks and multivariate process parameters

Abstract

This paper describes results of the application of feed-forward Multi-Layer Perceptron (MLP) neural networks for cutting tool state identification in a metal turning operation. Test cuts were conducted using P25 carbide inserts with and without wear (i.e. nominally sharp) on EN24 alloy steel. The acquired data were used to train, cross-validate and test the generalisation capabilities of two MLP configurations. Both networks had exactly the same input and output nodes but differing number of nodes in a single middle layer. Training was achieved via back-propagation of error enhanced by the addition of a momentum term and adaptive learning rate. Different error goal targets during training of the MLP were used, and the validation results of the model investigation analysed and presented. Obtained results for successful classification of the tool state with respect to only two classes (worn or sharp) were between 83 and 96%.