Machine Learning for Composites

Parameterization-Based Neural Network: Predicting Non-Linear Stress-Strain Response from Composite Microstructure

Composite materials, like syntactic foams, have complex internal microstructures that manifest high-stress concentrations due to material discontinuities between reinforcements and the binding media. Predicting the mechanical response as non-linear stress-strain curves of such heterogeneous materials from their microstructure is a challenging problem. This is true since various parameters, including the distribution and geometric properties of microballoons, dictate their response to mechanical loading. This paper presents a novel Neural Network (NN) framework called Parameterization-based Neural Network (PBNN), where we relate the composite microstructure to the non-linear response through this trained NN model. PBNN represents the stress-strain curve as a parameterized function to reduce the prediction size and predicts the function parameters for different syntactic foam microstructures.

Deep Learning for Stress Prediction in Heterogeneous Media

Stress analysis of heterogeneous media, like architected and composite materials, using finite element method (FEM) has become commonplace in design and analysis. However, determining stress distributions in heterogeneous media using FEM can be computationally expensive in situations like optimization and multi-scaling, where several design iterations are required to be tested iteratively until convergence.

To address this, we utilize Deep Learning for developing a set of novel Difference-based Neural Network (DiNN) frameworks based on engineering and statistics knowledge to determine stress distribution in heterogeneous media, for the first time, with special focus on discontinuous domains that manifest high stress concentrations.