| About this Abstract |
| Meeting |
TMS Specialty Congress 2025
|
| Symposium
|
8th World Congress on Integrated Computational Materials Engineering (ICME 2025)
|
| Presentation Title |
"Advanced Calibration of GTN Damage Model for Aluminum Alloy AA6111 Using Digital Image Correlation and Bayesian Inference" |
| Author(s) |
Seyed Mohammad Ali Seyed Mahmoud, Dominic Renner, Ali Khosravani, Surya Kalidindi |
| On-Site Speaker (Planned) |
Seyed Mohammad Ali Seyed Mahmoud |
| Abstract Scope |
This study characterizes the tensile behavior of aluminum alloy AA6111, an essential material in automotive forming processes. Predicting failure mechanisms under realistic conditions is challenging due to the complexity of deformation and damage in aluminum alloys. We present a novel approach to calibrate the Gurson-Tvergaard-Needleman (GTN) damage model by integrating tensile testing with Digital Image Correlation (DIC) for high-resolution strain field measurements. Our methodology involves a two-step Bayesian calibration: first, a Gaussian process surrogate model is trained on finite element simulations with material parameters (elastic, plastic, and GTN characteristics) and tensile response data (load-displacement curve and DIC strain field); then, inverse sampling of material parameters is conducted based on the tensile response. This Bayesian framework, combined with precise strain data, enables robust model calibration and validation, as well as uncertainty quantification of GTN parameters. The adaptable DIC-based calibration framework shows potential for broader applications across novel materials and models. |
| Proceedings Inclusion? |
Undecided |