A Statistical Perspective for Predicting the Strength of Metals: Revisiting the Hall-Petch Relationship using Machine Learning

TL;DR

This paper introduces a machine learning framework that predicts the strength of polycrystalline metals by accounting for microstructural variability, validating the Hall-Petch relationship across diverse microstructures with extensive data and probabilistic modeling.

Contribution

The study develops a probabilistic machine learning approach with large simulated microstructural data to validate and quantify the Hall-Petch relationship's applicability to real materials.

Findings

The machine learning model accurately predicts flow stress across microstructural variations.

The Hall-Petch relationship is statistically valid for correlating strength with grain size.

Microstructural features other than grain size have lesser importance in determining strength.

Abstract

The mechanical properties of a material are intimately related to its microstructure. This is particularly important for predicting mechanical behavior of polycrystalline metals, where microstructural variations dictate the expected material strength. Until now, the lack of microstructural variability in available datasets precluded the development of robust physics-based theoretical models that account for randomness of microstructures. To address this, we have developed a probabilistic machine learning framework to predict the flow stress as a function of variations in the microstructural features. In this framework, we first generated an extensive database of flow stress for a set of over a million randomly sampled microstructural features, and then applied a combination of mixture models and neural networks on the generated database to quantify the flow stress distribution and the relative importance of microstructural features. The results show excellent agreement with experiments and demonstrate that across a wide range of grain size, the conventional Hall-Petch relationship is statistically valid for correlating the strength to the average grain size and its comparative importance versus other microstructural features. This work demonstrates the power of the machine-learning based probabilistic approach for predicting polycrystalline strength, directly accounting for microstructural variations, resulting in a tool to guide the design of polycrystalline metallic materials with superior strength, and a method for overcoming sparse data limitations.