Estimation of Texture-Dependent Stress-Strain Curve and <i>r</i>-Value of Aluminum Alloy Sheet Using Deep Learning

Abstract

The deformation of an aluminum alloy sheet is affected by its underlying crystallographic texture and has been extensively studied using the crystal plasticity finite element method (CPFEM). Numerical material test based on the CPFEM enables the quantitative estimation of the stress-strain curve and Lankford value (r-value), which depend upon the texture of aluminum alloy sheets. However, the application of CPFEM-based numerical material test to the optimization of aluminum alloy texture is computationally expensive. In this paper, we propose a method for rapidly estimating the stress-strain curves and r-values of aluminum alloy sheets using deep learning with a neural network. We train the neural network with the synthetic crystallographic texture and stress-strain curves calculated through the numerical material tests. To capture the features of synthetic texture from a {111} pole-figure image, the neural network incorporates a convolution neural network. Using the trained neural network, we estimate the uniaxial stress-strain curve and in-plane anisotropy of the r-value for various textures that contain Cube and S components. The results indicate that the application of a neural network trained with the results of numerical material test is a promising method for rapidly estimating the deformation of aluminum alloy sheets.