Machine learning-enabled identification of micromechanical stress and strain hotspots predicted via dislocation density-based crystal plasticity simulations

Adnan Eghtesad, Qixiang Luo, Shun Li Shang, Ricardo A. Lebensohn, Marko Knezevic, Zi Kui Liu, Allison M. Beese

Research output: Contribution to journalArticlepeer-review

6 Scopus citations

Abstract

The present work uses a full-field crystal plasticity model with a first principles-informed dislocation density (DD) hardening law to identify the key microstructural features correlated with micromechanical fields localization, or hotspots, in polycrystalline Ni. An ensemble learning approach to machine learning interpreted with Shapley additive explanation was implemented to predict nonlinear correlations between microstructural features and micromechanical stress and strain hotspots. Results reveal that regions within the microstructure in the vicinity of grain boundaries, higher Schmid factors, low slip transmissions and high intergranular misorientations, are more prone to being micromechanical hotspots. Additionally, under combined loading and large plastic deformations, slip transmissions take precedence over intergranular misorientations in formation of both strain and stress hotspots. The present work demonstrates a successful integration of physics-based crystal plasticity with DD-based hardening into machine learning models to reveal the microscale features responsible for the formation of local stress and strain hotspots within the grains and near the grain boundaries, as function of applied deformation states, grain morphology/size distribution, and microstructural texture, providing insights into micromechanical damage initiation zones in polycrystalline metals.

Original languageEnglish (US)
Article number103646
JournalInternational journal of plasticity
Volume166
DOIs
StatePublished - Jul 2023

All Science Journal Classification (ASJC) codes

  • General Materials Science
  • Mechanics of Materials
  • Mechanical Engineering

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