An investigation of artificial neural network structure and its effects on the estimation of the low-cycle fatigue parameters of various steels

Abstract

Artificial neural networks (ANNs) are a widely used machine learning approach for estimating low-cycle fatigue parameters. ANN structure has its parameters such as hidden layers, hidden neurons, activation functions, training functions, and so forth, and these parameters have a significant influence over the results. Three hidden layer combinations, the hidden neurons ranging from 1 to 25, and different activation functions like hyperbolic tangent sigmoid (tansig), logistic sigmoid (logsig), and linear (purelin) were used, and their effects on the low-cycle fatigue parameter estimation were investigated to determine optimal ANN structure. Based on the results, suggestions regarding ANN structure for the estimation of the low-cycle fatigue parameters and transition fatigue life were presented. For the output layer and hidden layers, the most suitable activation function was tansig. The optimal hidden neuron range has been found between 4 and 9. The neural network structure with one hidden layer was determined to be most suitable in terms of less knowledge, structural complexity, and computational time and power.