|About this Abstract
||2019 TMS Annual Meeting & Exhibition
||Computational Approaches for Big Data, Artificial Intelligence and Uncertainty Quantification in Computational Materials Science
||M-8: Prediction of Biaxial Tensile Deformation Behavior of Aluminum Alloy Using Crystal Plasticity Finite Element Method and Machine Learning
||Kohta Koenuma, Akinori Yamanaka, Toshihiko Kuwabara
|On-Site Speaker (Planned)
We have proposed a numerical material testing method based on crystal plasticity finite element method (CPFEM) to predict biaxial tensile deformation behavior of aluminum alloy sheets. The previous study demonstrated that the calculated biaxial stress-strain (SS) curves showed good agreements with those measured by the biaxial tensile test with a cruciform specimen. However, the CPFEM requires very large computational resources to calculate SS curves for multiple linear stress paths. In this study, we propose an efficient simulation technique to predict the biaxial tensile deformation behavior of aluminum alloy sheets by coupling the CPFEM with machine learning techniques.
||Planned: Supplemental Proceedings volume