| About this Abstract |
| Meeting |
2025 TMS Annual Meeting & Exhibition
|
| Symposium
|
Verification, Calibration, and Validation Approaches in Modeling the Mechanical Performance of Metallic Materials
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| Presentation Title |
Advanced Calibration of the GTN Damage Model for Aluminum Alloy AA6111 via Bayesian Inference and Digital Image Correlation Techniques |
| Author(s) |
Seyed Mohammad Ali Seyed Mahmoud, Dominic Renner, Raj Mahat, Ali Khosravani, Surya Kalidindi |
| On-Site Speaker (Planned) |
Seyed Mohammad Ali Seyed Mahmoud |
| Abstract Scope |
This research aims to characterize the tensile behavior of aluminum alloy AA6111, crucial for automotive forming applications. Accurately predicting the failure regime under realistic conditions is challenging due to complex material deformation and damage. We introduce a novel methodology for calibrating the Gurson-Tvergaard-Needleman (GTN) damage model using tensile tests and Digital Image Correlation (DIC) to capture detailed strain fields. The calibration process involves a two-step Bayesian approach: first, a Gaussian process surrogate model is trained on finite element simulations with material parameters (elastic, plastic, and GTN) and tensile response (load-displacement curve and DIC strain field); second, inverse sampling of the material parameters given the tensile response. This high-resolution strain measurement and Bayesian approach provide a robust framework for model calibration and validation, enabling uncertainty quantification of GTN parameters. The framework can be adapted for novel materials and models, broadening its impact. |
| Proceedings Inclusion? |
Planned: |
| Keywords |
Mechanical Properties, Aluminum, Machine Learning |