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

摘要

This paper describes results of the application of feed-forward Multi-Layer Perceptron (MLP) neuralnetworks for cutting tool state identification in a metal turning operation. Test cuts were conductedusing P25 carbide inserts with and without wear (i.e. nominally sharp) on EN24 alloy steel. Theacquired data were used to train, cross-validate and test the generalisation capabilities of two MLPconfigurations. Both networks had exactly the same input and output nodes but differing number ofnodes in a single middle layer. Training was achieved via back-propagation of error enhanced by theaddition of a momentum term and adaptive learning rate. Different error goal targets during trainingof 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 (wornor sharp) were between 83 and 96%.