ANN-based performance prediction of electrical discharge machining of Ti-13Nb-13Zr alloys

PurposeThe purpose of this paper is to predict the machining performance of electrical discharge machining of Ti-13Nb-13Zr (TNZ) alloy, a promising biomedical alloy, using artificial neural networks (ANN) models. Design/methodology/approachIn the research, three major performance characteristics, i.e. the material removal rate (MRR), tool wear rate (TWR) and surface roughness (SR), were chosen for the study. The input parameters for machining were the voltage, current, pulse-on time and pulse-off time. For the ANN model, a two-layer feedforward network with sigmoid hidden neurons and linear output neurons were chosen. Levenberg-Marquardt backpropagation algorithm was used to train the neural networks. FindingsThe optimal ANN structure comprises four neurons in input layer, ten neurons in hidden layer and one neuron in the output layer (4-10-1). In predicting MRR, the 60-20-20 data split provides the lowest MSE (0.0021179) and highest R-value for training (0.99976). On the contrary, the 70-15-15 data split results in the best performance in predicting both TWR and SR. The model achieves the lowest MSE and highest R-value for training in predicting TWR as 1.17E-06 and 0.84488, respectively. Increasing the number of hidden neurons of the network further deteriorates the performance. In predicting SR, the authors find the best MSE and R-value as 0.86748 and 0.94024, respectively. Originality/valueThis is a novel approach in performance prediction of electrical discharge machining in terms of new workpiece material (TNZ alloys).