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Meeting 2024 TMS Annual Meeting & Exhibition
Symposium AI/Data Informatics: Computational Model Development, Verification, Validation, and Uncertainty Quantification
Presentation Title High-throughput Micromechanical Simulations Framework and Its Applications to Predict Microstructure-property Relationships Using a Machine Learning Approach
Author(s) Napat Vajragupta, Abhishek Biswas, Jihad Zraibi, Hitesh Walia, Marzuk Kamal, Tatu Pinomaa, Matti Lindroos, Sicong Ren, Tom Andersson, Anssi Laukkanen
On-Site Speaker (Planned) Napat Vajragupta
Abstract Scope This work will present the high-throughput micromechanical simulation workflow and demonstrate its capability to produce data for developing a machine learning model for deriving microstructure-property relationships. The first application example aims to predict anisotropic mechanical behavior from a given crystallographic texture. We will generate micromechanical models with different crystallographic texture information, couple them with a phenomenological crystal plasticity model, and simulate them under various loading conditions to characterize anisotropic mechanical behavior virtually. We will also propose a metadata schema for describing micromechanical simulations, which assists machine learning model development. Data generated will be used to train and test a machine learning model for predicting anisotropic mechanical behavior from the description of crystallographic texture. In the second application example, we will apply a similar workflow to correlate how the defect morphology affects the fatigue properties of metals.
Proceedings Inclusion? Planned:
Keywords ICME, Mechanical Properties, Machine Learning

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