Applied Machine Learning to Predict Stress Hotspots II: Hexagonal close packed materials

TL;DR

This paper applies machine learning to predict stress concentration regions in hexagonal close packed materials, demonstrating high accuracy and identifying microstructural features influencing hotspot formation under tensile stress.

Contribution

It introduces a machine learning framework for predicting stress hotspots in HCP materials and analyzes the impact of slip system preferences on stress concentration.

Findings

Random forest models predict hotspots with over 0.8 AUC score.

Microstructural features can be identified as key factors in hotspot formation.

The approach works for different slip system ratios in HCP materials.

Abstract

Stress hotspots are regions of stress concentrations that form under deformation in polycrystalline materials. We use a machine learning approach to study the effect of preferred slip systems and microstructural features that reflect local crystallography, geometry, and connectivity on stress hotspot formation in hexagonal close packed materials under uniaxial tensile stress. We consider two cases: one without any preferred slip systems with a critically resolved shear stress (CRSS) ratio of 1:1:1, and a second with CRSS ratio 0.1:1:3 for basal: prismatic: pyramidal slip systems. Random forest based machine learning models predict hotspot formation with an AUC (area under curve) score of 0.82 for the Equal CRSS and 0.81 for the Unequal CRSS cases. The results show how data driven techniques can be utilized to predict hotspots as well as pinpoint the microstructural features causing stress hotspot formation in polycrystalline microstructures