Modeling plasticity-mediated void growth at the single crystal scale: A physics-informed machine learning approach

Garbrecht, Karl Michael; Rovinelli, Andrea; Hochhalter, Jacob; Christodoulou, Paul; Lebensohn, Ricardo A.; Capolungo, Laurent

doi:10.1016/j.mechmat.2024.105151

Modeling plasticity-mediated void growth at the single crystal scale: A physics-informed machine learning approach

Journal Article · Tue Sep 10 00:00:00 EDT 2024 · Mechanics of Materials

DOI:https://doi.org/10.1016/j.mechmat.2024.105151· OSTI ID:2440686

^[1]; ^[1]; Hochhalter, Jacob ^[2]; Christodoulou, Paul ^[3]; ^[1]; ^[1]

Los Alamos National Laboratory (LANL), Los Alamos, NM (United States)
Univ. of Utah, Salt Lake City, UT (United States)
Univ. of California, Santa Barbara, CA (United States)

Modeling the evolution of voids during plastic flow as well as their effects on plastic dissipation is critical for both component manufacturing and lifetime estimation purposes. To this end, we propose a rate-dependent constitutive model to homogenize the effects of semi-randomly distributed voids on single crystal plasticity whilst capturing void interaction and plastic anisotropy. Here, this present work focuses on the case of face centered cubic crystals to introduce an anisotropic gauge function applicable within the crystal plasticity formalism. The approach combines analytical methods to describe the micromechanics of the system in combination with symbolic regression to capture analytically intractable mechanisms from data. The hybrid framework uses a physics-informed genetic programming-based symbolic regression algorithm to solve a multiform optimization problem simultaneously producing a new gauge function and a new strain rate equation. This is also a multi-objective optimization problem with many competing objectives. A new search and selection step is introduced to the genetic algorithm that promotes convergence toward a global solution that better satisfies all the objectives. Overall, the symbolic equations produced leverage data-driven methods to achieve greater accuracy than comparable alternatives on an analytically intractable problem while maintaining model transparency.

View Accepted Manuscript (DOE)

Research Organization:: Los Alamos National Laboratory (LANL), Los Alamos, NM (United States)

Sponsoring Organization:: USDOE Office of Fossil Energy and Carbon Management (FECM), Office of Carbon Management

Grant/Contract Number:: 89233218CNA000001

OSTI ID:: 2440686

Report Number(s):: LA-UR--24-20692

Journal Information:: Mechanics of Materials, Journal Name: Mechanics of Materials Vol. 199; ISSN 0167-6636

Publisher:: ElsevierCopyright Statement

Country of Publication:: United States

Language:: English

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