Competition of systematically optimized deep neural networks for the estimation of tensile behavior of aluminum-magnesium alloy

Abstract

Aluminum-magnesium (Al-Mg) alloys are prevalently employed within the aerospace sector. This research engaged a suite of deep learning approaches, encompassing the Artificial Neural Network (ANN), Gated Recurrent Unit (GRU) networks, Long-Short Term Memory (LSTM), and simple Recurrent Neural Network (RNN) to evaluate their predictive efficacy regarding the tensile strength and stiffness of Al-Mg alloys obtained from molecular dynamics simulation. The Taguchi method was initially applied to refine the architecture of each deep neural network (DNN), followed by a comparative analysis of their optimized configurations. The findings of this investigation revealed that the refined simple RNN and LSTM models exhibited superior predictive accuracy for estimating the strength and stiffness of the alloy, respectively. Moreover, the study elucidated that DNNs equipped with memory capabilities outstripped traditional ANNs in forecasting the tensile properties of Al-Mg alloys.