Uncertainty-Aware Machine-Learning Framework for Predicting Dislocation Plasticity and Stress-Strain Response in FCC Alloys

TL;DR

This paper introduces an uncertainty-aware machine learning framework using a mixed density network to predict dislocation behavior and stress responses in FCC alloys, enhancing material design accuracy.

Contribution

It presents a novel MDN-based approach that predicts distributional properties of dislocation density and stress, integrating uncertainty quantification into plasticity modeling.

Findings

Improved stress-strain prediction accuracy.

Explicit uncertainty quantification in predictions.

Enhanced alloy design optimization.

Abstract

Machine learning has significantly advanced the understanding and application of structural materials, with an increasing emphasis on integrating existing data and quantifying uncertainties in predictive modeling. This study presents a comprehensive methodology utilizing a mixed density network (MDN) model, trained on extensive experimental data from literature. This approach uniquely predicts the distribution of dislocation density, inferred as a latent variable, and the resulting stress distribution at the grain level. The incorporation of statistical parameters of those predicted distributions into a dislocation-mediated plasticity model allows for accurate stress-strain predictions with explicit uncertainty quantification. This strategy not only improves the accuracy and reliability of mechanical property predictions but also plays a vital role in optimizing alloy design, thereby facilitating the development of new materials in a rapidly evolving industry.