Optimizing crystal plasticity model parameters via machine learning-based optimization algorithms
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A4 Artikkeli konferenssijulkaisussa
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Material Forming: The 26th International ESAFORM Conference on Material Forming - ESAFORM 2023 - held in Kraków, Poland, April 19-21, 2023, pp. 1417-1426, Materials Research Proceedings ; Volume 28
Abstract
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.Description
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Juan, R, Nguyen Xuan, B, Liu, W & Lian, J 2023, Optimizing crystal plasticity model parameters via machine learning-based optimization algorithms. in L Madej, M Sitko & K Perzynsk (eds), Material Forming: The 26th International ESAFORM Conference on Material Forming - ESAFORM 2023 - held in Kraków, Poland, April 19-21, 2023., 153, Materials Research Proceedings, vol. 28, Materials Research Forum LLC, pp. 1417-1426, International ESAFORM Conference on Material Forming, Kraków, Poland, 19/04/2023. https://doi.org/10.21741/9781644902479-153