Stochastic reconstruction of multiphase composite microstructures using statistics-encoded neural network for poro/micro-mechanical modelling

TL;DR

This paper presents a neural network-based framework that efficiently reconstructs statistically equivalent 3D microstructures of multiphase composites from limited 2D data, enabling cost-effective microstructure-property analysis.

Contribution

Introduces a novel statistics-encoded neural network (SENN) framework for 3D microstructure reconstruction from limited 2D exemplars, reducing reliance on extensive volumetric datasets.

Findings

Reconstructed microstructures show strong statistical similarity to reference data.

Validated microstructures accurately predict macroscopic properties like stiffness and permeability.

Framework is computationally efficient and adaptable across different composite types.

Abstract

Understanding microstructure-property relationships (MPRs) is essential for optimising the performance of multiphase composites. Image-based poro/micro-mechanical modelling provides a non-invasive approach to exploring MPRs, but the randomness of multiphase composites often necessitates extensive 3D microstructure datasets for statistical reliability. This study introduces a cost-effective machine learning framework to reconstruct numerous virtual 3D microstructures from limited 2D exemplars, circumventing the high costs of volumetric microscopy. Using feedforward neural networks, termed the statistics-encoded neural network (SENN), the framework encodes 2D morphological statistics and infers 3D morphological statistics via a 2D-to-3D integration scheme. Statistically equivalent 3D microstructures are synthesised using Gibbs sampling. Hierarchical characterisation enables seamless capture of features across multiple scales. Validation on three composites demonstrates strong statistical equivalence between reconstructed and reference microstructures, confirmed by morphological descriptors and simulated macroscopic properties (e.g., stiffness, permeability). The SENN-based framework is a high-fidelity tool for efficiently and accurately reconstructing multiphase microstructures.