Predict the evolution of mechanical property of Al-Li alloys in a marine environment

Abstract

The ocean is one of the essential fields of national defense in the future, and more and more attention is paid to the lightweight research of Marine equipment and materials. This study it is to develop a Machine learning (ML)-based prediction method to study the evolution of the mechanical properties of Al-Li alloys in the marine environment. We obtained the mechanical properties of Al-Li alloy samples under uniaxial tensile deformation at different exposure times through Marine exposure experiments. We obtained the strain evolution by digital image correlation (DIC). The strain field images are voxelized using 2D-Convolutional Neural Networks (CNN) autoencoders as input data for Long Short-Term Memory (LSTM) neural networks. Then, the output data of LSTM neural networks combined with corrosion features were input into the Back Propagation (BP) neural network to predict the mechanical properties of Al-Li alloys. The main conclusions are as follows: 1. The variation law of mechanical properties of 2297-T8 in the Marine atmosphere is revealed. With the increase in outdoor exposure test time, the tensile elastic model of 2297-T8 changes slowly, within 10%, and the tensile yield stress changes significantly, with a maximum attenuation of 23.6%. 2. The prediction model can predict the strain evolution and mechanical response simultaneously with an error of less than 5%. 3. This study shows that a CNN/LSTM system based on machine learning can be built to capture the corrosion characteristics of Marine exposure experiments. The results show that the relationship between corrosion characteristics and mechanical response can be predicted without considering the microstructure evolution of metal materials.