Abstract
Fatigue in metallic materials is a cost-intensive engineering challenge due to unpredictable occurrences when undergoing cyclic loading. Particularly vulnerable is dwell fatigue, where the hold time significantly reduces life. The unpredictability is attributed to microstructure-dependent crack nucleation (a significant portion of life) in polycrystalline microstructures with evolving mechanisms. In titanium alloys like Ti-6Al-4V, microstructural heterogeneity, including micro-textured regions, anisotropic crystallographic properties, and strain-rate dependence play key roles in fatigue crack evolution. This paper develops a spatiotemporal multiscale computational platform for predicting the probabilistic manifestations of component-scale dwell fatigue crack nucleation, with linkages to the location-specific underlying 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 experimentally-validated computational platform show its promise in multiscale fatigue predictions, exploring the competing effects of geometry and microstructure with loading. It also demonstrates the effectiveness of specimen test data-calibrated PUCM-PUCNMs in component fatigue predictions, demonstrating its promise for fatigue-resistant structure-material design.
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Data availability
All relevant datasets have been shared on the general repository figshare and published at https://doi.org/10.6084/m9.figshare.31626442.v1. The input data used to generate 3D M-SERVEs are in the SERVE_construction folder. The relevant raw data for each figure and the files to visualize the microstructure are provided in figshare. Source data are provided with this paper.
Code availability
Compiled executables of codes used to generate the results of the study are available upon request to the corresponding author.
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Acknowledgements
This work has been sponsored by a grant from the Air Force Office of Scientific Research, Structural Mechanics and Prognosis Program, USA, through grant No. FA9550-21-1-0321 (Program Director: Dr. Gregg Abate). The authors gratefully acknowledge the support of this work. The LCF tests and EBSD data were previously supported by the Air Force Materials Affordability Initiative (MAI) program project on titanium texture and fatigue under a Cooperative Agreement FA8650-17-2-5266 led by Pratt & Whitney. This support is gratefully acknowledged. Computational support for this work has been provided by the Advanced Research Computing at Hopkins (ARCH) core facility. Computational support for this work is also provided by an AFOSR DURIP grant FA9550-21-1-0303.
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S.G. has initiated and directed the study, provided the conceptual framework, and has written the majority of the paper. K.A.N., T.N.T., and S.K. have developed models, codes, and conducted the simulations, as well as written drafts of the paper. A.P., V.V., and D.F. have contributed the experimental results and the vision, as well as edited the manuscript.
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Ghosh, S., Appunhi Nair, K., Tak, T.N. et al. Parametrically upscaled model-based predictive platform for fatigue with location-specific microstructural linkages. Nat Commun (2026). https://doi.org/10.1038/s41467-026-72037-z
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DOI: https://doi.org/10.1038/s41467-026-72037-z
