| About this Abstract |
| Meeting |
MS&T24: Materials Science & Technology
|
| Symposium
|
Additive Manufacturing Modeling, Simulation, and Machine Learning: Microstructure, Mechanics, and Process
|
| Presentation Title |
Microstructure-Sensitive Fatigue Models from Micromechanical Fatigue Experiments |
| Author(s) |
Peter Gumbsch, Ali Riza Durmaz, Akhil Riza Thomas, Thomas Riza Straub, Chris Riza Eberl |
| On-Site Speaker (Planned) |
Peter Gumbsch |
| Abstract Scope |
High cycle fatigue life and is sensitive to microstructural details and governed by crack initiation. While microstructure-sensitive models are available, their validation is difficult. We have therefore developed a combined experimental and data post-processing workflow to establish multimodal observation of fatigue crack initiation and propagation efficiently. It involves fatigue testing of mesoscale specimens, data fusion through multimodal registration, and image-based data-driven damage localization. We then propose a validation framework where a fatigue test is mimicked in a sub-modeling simulation by embedding the measured microstructure into the specimen geometry and adopting an approximation of the experimental boundary conditions. This simulation-based approach is compared to training graph convolutional networks on the single grain level. Such graph convolutional networks yielded the best performance with a balanced accuracy of 0.72 and a F1-score of 0.34, outperforming the phenomenological crystal plasticity simulations and conventional machine learning models by large margins. |