Engineering Neutron Diffraction Data Analysis with Inverse Neural Network Modeling

Abstract

Integration of engineering neutron diffraction data analysis and solid mechanics modeling is a powerful method to deduce in-situ constitutive behavior of materials. Since diffraction data originates from spatially discrete subsets of the material volume, extrapolation of the data to the behavior of the overall sample is non-trivial. The finite element model has been widely used for interpreting diffraction data by optimizing model parameters via iterative processes. In order to maximize the rigor of such analysis and to increase fitting efficiency and accuracy, we have developed an optimization algorithm based on the neural network architecture. The inverse neural network model reveals parameter sensitivity quantitatively during a training process, and yields more accurate phase specific constitutive laws of the composite materials compared to the conventional method once networks are successfully trained.