Evaluation of neural network parameters in the prediction of AISI 1050 steel machining performance

Abstract

ABSTRACT Significant improvement in the design of ANN model has been hindered by the lack of evaluation of alternate model parameters. It is important that in generating a suitable ANN model, the best parameters capable of providing the fastest network and the most accurate test result be employed. In this study, the performance of the NN models based on the most suitable NN parameters such as the number of hidden layer neurons ranging from 2–20, activation functions; sigmoid (logsig), tangent sigmoid (tansig), and the linear (purelin), along with three different training algorithm (LM, SCG, and RP), has been evaluated to accurately predict the response values; metal removal rate (MRR) and tool wear in the turning operation of AISI 1050 steel bar. A great difference was observed in the performance of the activation functions and in the training algorithms. The optimal ANN model prediction, recorded R values of 0.827, 0.920 and 0.983 for the training, validation and testing sub-datasets respectively, while an overall R-value of 0.876 was obtained for the training process, signifying confirmation of the predictive ability of the model. The experiments demonstrate that the identification of the most suitable activation functions can lead to significant performance improvements without actually increasing the number of neurons.