# Microstructural Inelastic Fingerprints And Data-Rich Predictions of Plasticity and Damage in Solids

## TL;DR

This paper introduces a novel framework that uses sequences of microstructural images to create inelastic fingerprints, enabling data-rich predictions of plasticity and damage in solids through deep learning techniques.

## Contribution

It develops a microstructural fingerprinting method that predicts mechanical responses from images, combining image recognition with inelastic response modeling.

## Key findings

- Fingerprints can reconstruct mechanical responses of unknown microstructures.
- Deep neural networks effectively predict inelastic behaviors from microstructural images.
- Method demonstrates scalability and robustness in phase field simulations.

## Abstract

Inelastic mechanical responses in solids, such as plasticity, damage and crack initiation, are typically modeled in constitutive ways that display microstructural and loading dependence. Nevertheless, {linear} elasticity at infinitesimal deformations is used for microstructural properties. We demonstrate a framework that builds on sequences of microstructural images to develop fingerprints of inelastic tendencies, and then use them for data-rich predictions of mechanical responses up to failure. In analogy to common fingerprints, we show that these two-dimensional instability-precursor signatures may be used to reconstruct the full mechanical response of unknown sample microstructures; this feat is achieved by reconstructing appropriate average behaviors with the assistance of a deep convolutional neural network that is fine-tuned for image recognition. We demonstrate basic aspects of microstructural fingerprinting in a toy model of dislocation plasticity and then, we illustrate the method's scalability and robustness in phase field simulations of model binary alloys under mode-I fracture loading.