Parameterization-based Neural Network: Predicting Non-linear Stress-Strain Response of Composites

TL;DR

This paper introduces a novel neural network framework called PBNN that predicts the non-linear stress-strain response of composite materials by parameterizing the response function based on microstructural features, enabling accurate predictions with smaller datasets.

Contribution

The paper presents a new parameterization-based neural network model that effectively predicts non-linear stress-strain curves of heterogeneous composites from microstructure data, reducing data requirements.

Findings

PBNN accurately predicts stress-strain responses across various microstructures.

The model outperforms baseline models in prediction accuracy with limited data.

High-level geometric features improve the neural network's predictive capability.

Abstract

Composite materials like syntactic foams have complex internal microstructures that manifest high-stress concentrations due to material discontinuities occurring from hollow regions and thin walls of hollow particles or microballoons embedded in a continuous medium. 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. To that end, 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. We show that compared to several common baseline models considered in this paper, the PBNN can accurately predict non-linear stress-strain responses and the corresponding parameterized functions using smaller datasets. This is enabled by extracting high-level features from the geometry data and tuning the predicted response through an auxiliary term prediction. Although built in the context of the compressive response prediction of syntactic foam composites, our NN framework applies to predict generic non-linear responses for heterogeneous materials with internal microstructures. Hence, our novel PBNN is anticipated to inspire more parameterization-related studies in different Machine Learning methods.