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Meeting 2025 TMS Annual Meeting & Exhibition
Symposium Artificial Intelligence Applications in Integrated Computational Materials Engineering
Presentation Title Effect of the Microstructure on Intergranular Fracture in FCC and HCP Polycrystals: A Machine Learning Approach
Author(s) Javier Llorca
On-Site Speaker (Planned) Javier Llorca
Abstract Scope The effect of microstructural features (grain size, grain boundary misorientation, texture) on the mechanisms of intergranular damage nucleation was assessed via finite element simulations in FCC and HCP polycrystals. Single crystals were modelled using a dislocation-based crystal plasticity model and intergranular fracture was introduced through cohesive surfaces at the grain boundaries in thin foil polycrystals. Furthermore, the model took into account whether the grain boundaries were opaque (slip transfer was blocked), transparent (slip transfer was allowed), or translucent (slip transfer was allowed between well-aligned systems) depending on geometrical criteria. The information on damage nucleation from simulations was analyzed using machine learning. Fracture in FCC polycrystals was triggered when the grain boundary was perpendicular to the loading axis and slip transfer was blocked. In the case of HCP polycrystals, damage nucleation was also triggered by the grain boundary orientation and by the crystallographic orientation of the grains.
Proceedings Inclusion? Planned:
Keywords Mechanical Properties, Computational Materials Science & Engineering, Machine Learning

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Effect of the Microstructure on Intergranular Fracture in FCC and HCP Polycrystals: A Machine Learning Approach
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