About this Abstract |
Meeting |
2025 TMS Annual Meeting & Exhibition
|
Symposium
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AI/Data Informatics: Computational Model Development, Verification, Validation, and Uncertainty Quantification
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Presentation Title |
High-Throughput Bayesian Calibration of Elastic-Plastic-Damage Model Parameters Using a Small Punch Test |
Author(s) |
Raj Jung Mahat, Surya Kalidindi |
On-Site Speaker (Planned) |
Raj Jung Mahat |
Abstract Scope |
Small Punch Test (SPT) has been explored as a high throughput alternative for the evaluation of mechanical response of metals. A central challenge lies in the estimation of material constitutive parameters (e.g., modulus, yield strength, ultimate tensile strength, ductility) from the measurements produced in SPT. The present study demonstrates novel data analyses protocols for calibrating an elastic-plastic continuum damage mechanics model to the SPT measurements. Specifically, we explore the feasibility of calibrating the Gurson-Tvergaard-Needleman (GTN) damage constitutive material model to the measured load-displacement and the 3-D Digital Image Correlation (DIC) strain fields measured in the SPT. |
Proceedings Inclusion? |
Planned: |
Keywords |
Machine Learning, Mechanical Properties, Characterization |