About this Abstract |
Meeting |
2023 TMS Annual Meeting & Exhibition
|
Symposium
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AI/Data Informatics: Computational Model Development, Validation, and Uncertainty Quantification
|
Presentation Title |
Prediction of Slip Localization and Transmission in Polycrystalline HCP Metals via Incorporation of Micromechanical Modeling and Machine Learning |
Author(s) |
Behnam Ahmadikia, Adolph L. Beyerlein, Irene J. Beyerlein |
On-Site Speaker (Planned) |
Behnam Ahmadikia |
Abstract Scope |
Slip bands (SBs) play a significant role in deformation of HCP metals since they accommodate a large share of slip while occupying a small volume fraction. Intense stress fields developed by SBs act as potential sites for crack nucleation or cause SBs to transmit across the grain boundaries, leading to long SB chains that percolate across the microstructure. While slip localization in HCPs is reported to be influenced by many factors, including material, texture, and microstructural features, the significance of each has not yet been investigated. We use an explicit SB full-field FFT-based model to verify the variations in intensity of slip localization and its transmission propensity under different circumstances. Then, we apply a Random Forest classifier and a Neural Network to a dataset of 3D microstructures developing SBs to identify the most influential factors and discuss the design parameters that hinder serial transmission of SBs in HCP polycrystals. |
Proceedings Inclusion? |
Planned: |
Keywords |
Machine Learning, Mechanical Properties, Modeling and Simulation |