Abstract
This study addresses a machine learning (ML)-reinforced strategy to build both linear and non-linear property closures for metallic materials. A property closure is a closed space of material properties that contains all possible values of the closure variables. The material properties of metals are significantly dependent on the underlying microstructure texture. Here, the polycrystalline material is expressed by the Orientation Distribution Function (ODF) that relates to the volume densities of the crystallographic orientations. Theoretically, the property closures of volume-averaged material properties can be derived using single-crystal microstructure solutions; however, this theory is not valid for non-linear properties. Therefore, we use an ML-reinforced strategy to generate both linear and non-linear material property closures using the Linear Regression (LR) and Artificial Neural Network (ANN) method with Bayesian Regularization. The closures for material properties, such as the elastic stiffness parameters and critical buckling load, are generated for Titanium, Magnesium, and Aluminum. The outcomes of the ML surrogate models for these properties are compared to each other. The results demonstrate that the ANN model with Bayesian regularization is capable of predicting both linear and non-linear material properties with almost 100% accuracy. However, the linear regression algorithm is found to be not as accurate as of the Bayesian inference for the non-linear property even though it provides similar accuracy as ANN for the linear property. Therefore, ANN with Bayesian regularization is utilized for predicting the property closure of critical buckling load, which is a non-linear property.
NSTL
Elsevier