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Fatigue in Materials: Fundamentals, Multiscale Characterizations and Computational Modeling: On-Demand Oral Presentations
Sponsored by: TMS Materials Processing and Manufacturing Division, TMS Structural Materials Division, TMS: Additive Manufacturing Committee, TMS: Advanced Characterization, Testing, and Simulation Committee, TMS: Computational Materials Science and Engineering Committee, TMS: Integrated Computational Materials Engineering Committee, TMS: Mechanical Behavior of Materials Committee
Program Organizers: J.C. Stinville, University of Illinois Urbana-Champaign; Garrett Pataky, Clemson University; Ashley Spear, University of Utah; Antonios Kontsos, Drexel University; Brian Wisner, Ohio University; Orion Kafka, National Institute Of Standards And Technology

Monday 8:00 AM
March 14, 2022
Room: Materials Design
Location: On-Demand Room

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A Physics-based Machine Learning Approach to Microstructure-based Modeling of High Cycle Fatigue Life Prediction: Dong Qian¹; ¹The University of Texas at Dallas
    High cycle fatigue (HCF) is the dominant failure mechanism of many engineering applications. For fatigue life predictions safe-life and damage-tolerance approaches have been used extensively, however, they are limited due to the empirical nature. These limitations can be addressed by performing a direct numerical simulation of the fatigue loading history. These limitations motivate the development of a multiscale HCF simulation approach called the extended space-time finite element method (XTFEM) to be presented. To address the challenge in capturing nonlinear material behavior associated with material microstructures under the HCF loading condition, XTFEN is integrated with a microstructure-based HCF damage model based on machine learning using the HideNN-AI self-consistent clustering analysis. This implementation enables the direct modeling of complex material microstructures with much reduced computational cost. Finally, examples of HCF life prediction are presented to demonstrate the robustness of the proposed multiscale approach.

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