About this Abstract |
| Meeting |
2026 TMS Annual Meeting & Exhibition
|
| Symposium
|
Computational Materials for Qualification and Certification
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| Presentation Title |
Parametrically Upscaled Model-Based Predictive Platform for Fatigue with Location-Specific Microstructural Linkages |
| Author(s) |
Somnath Ghosh, K.A. Nair, Tawqeer Nasir Tak, Shravan Kotha, Adam L. Pilchak, V. Venkatesh, David U. Furrer |
| On-Site Speaker (Planned) |
Somnath Ghosh |
| Abstract Scope |
Fatigue in metallic materials is a cost-intensive engineering challenge due to unpredictable occurrences. Particularly vulnerable is dwell fatigue, where the hold time significantly reduces life. The unpredictability is attributed to microstructure-dependent crack nucleation in polycrystalline microstructures with evolving mechanisms. In titanium alloys, microstructural heterogeneity, including micro-textured regions, anisotropic crystallographic properties, and strain-rate dependence play key roles in fatigue crack evolution. This talk will discuss a spatiotemporal multiscale computational platform for predicting the probabilistic manifestations of component-scale dwell fatigue crack nucleation, with linkages to the location-specific microstructure. It integrates physics-based modeling, machine learning, temporal acceleration, and probabilistic analysis to introduce parametrically upscaled constitutive and crack nucleation models (PUCM-PUCNM) for efficient prediction at macro and micro scales. Nucleation studies with the computational platform show its promise in multiscale fatigue predictions, exploring the competing effects of geometry and microstructure. It also demonstrates the effectiveness of specimen test data-calibrated models in fatigue predictions. |
| Proceedings Inclusion? |
Planned: |