Simultaneous Optimization of Crystal Plasticity Hardening Parameters: Simultaneous Optimization of Crystal Plasticity Hardening Parameters: Shimanek, Liu, and Beese.
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| Title: | Simultaneous Optimization of Crystal Plasticity Hardening Parameters: Simultaneous Optimization of Crystal Plasticity Hardening Parameters: Shimanek, Liu, and Beese. |
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| Authors: | Shimanek, John D.1 (AUTHOR), Liu, Zi-Kui1 (AUTHOR), Beese, Allison M.1,2 (AUTHOR) amb961@psu.edu |
| Source: | JOM: The Journal of The Minerals, Metals & Materials Society (TMS). Jan2025, Vol. 77 Issue 1, p324-335. 12p. |
| Subjects: | Face centered cubic structure, Crystal models, Single crystals, Copper, Crystals |
| Abstract: | Crystal plasticity models relate macroscopic deformation behavior to the evolution of slip systems strength, but their parameterization is often non-unique, with multiple parameter sets being able to describe the same macroscopic behavior. To address this issue, the present work adopts a Bayesian optimization framework for the parameterization of face-centered cubic plasticity models while simultaneously considering multiple experimental datasets from the literature. For single crystal Cu, parameter optimization was guided by the tensile stress–strain curves along several crystallographic orientations, with an adequate fit being found for five orientations at once. While additional parameters allowed for the consideration of more physical mechanisms, like different slip system interaction strengths or misorientations inherent to the experimental data, the extra dimensionality was found to limit the efficiency of the global minimization procedure. For polycrystalline Ni, multiple grain sizes were considered together in a representative polycrystalline model, with the optimization able to reconcile the model with the data for three grain sizes at once. As meaningful interpretation of parameters relies on the uniqueness of their values, incorporating multiple datasets into this discerning parameterization procedure enables more robust prediction and application of crystal plasticity models. [ABSTRACT FROM AUTHOR] |
| Copyright of JOM: The Journal of The Minerals, Metals & Materials Society (TMS) is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Engineering Source |
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| Abstract: | Crystal plasticity models relate macroscopic deformation behavior to the evolution of slip systems strength, but their parameterization is often non-unique, with multiple parameter sets being able to describe the same macroscopic behavior. To address this issue, the present work adopts a Bayesian optimization framework for the parameterization of face-centered cubic plasticity models while simultaneously considering multiple experimental datasets from the literature. For single crystal Cu, parameter optimization was guided by the tensile stress–strain curves along several crystallographic orientations, with an adequate fit being found for five orientations at once. While additional parameters allowed for the consideration of more physical mechanisms, like different slip system interaction strengths or misorientations inherent to the experimental data, the extra dimensionality was found to limit the efficiency of the global minimization procedure. For polycrystalline Ni, multiple grain sizes were considered together in a representative polycrystalline model, with the optimization able to reconcile the model with the data for three grain sizes at once. As meaningful interpretation of parameters relies on the uniqueness of their values, incorporating multiple datasets into this discerning parameterization procedure enables more robust prediction and application of crystal plasticity models. [ABSTRACT FROM AUTHOR] |
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| ISSN: | 10474838 |
| DOI: | 10.1007/s11837-024-06935-2 |
