Optimizing crystal plasticity model parameters via machine learning-based optimization algorithms
JUAN Rongfei, BINH Nguyen Xuan, LIU Wenqi, LIAN Junhe
download PDFAbstract. The field of materials science and engineering is constantly evolving, and new methods are being developed to improve our understanding of the relationship between microstructure and properties. One such method is crystal plasticity (CP) modeling, which is widely used for predicting the mechanical properties of crystals and phases. However, determining the constitutive parameters for CP models has been a significant challenge, with current methods relying on either direct chemical composition or inverse fitting, both of which can be time-consuming and lack accuracy. In this study, we propose an automated, advanced, and more efficient method for determining constitutive parameters by using a genetic algorithm (GA) optimization method coupled with machine learning. Our proposed method is applied to two widely used CP models, and the reference data for the calibration is the stress-strain curve from tensile tests. The results of the automated calibration process are then compared to numerical simulation results of CP models with known parameters, demonstrating the efficiency and accuracy of our proposed method.
Keywords
Crystal Plasticity Model, Machine Learning, Parameter Calibration
Published online 4/19/2023, 10 pages
Copyright © 2023 by the author(s)
Published under license by Materials Research Forum LLC., Millersville PA, USA
Citation: JUAN Rongfei, BINH Nguyen Xuan, LIU Wenqi, LIAN Junhe, Optimizing crystal plasticity model parameters via machine learning-based optimization algorithms, Materials Research Proceedings, Vol. 28, pp 1417-1426, 2023
DOI: https://doi.org/10.21741/9781644902479-153
The article was published as article 153 of the book Material Forming
Content from this work may be used under the terms of the Creative Commons Attribution 3.0 license. Any further distribution of this work must maintain attribution to the author(s) and the title of the work, journal citation and DOI.
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