|About this Abstract
||2022 TMS Annual Meeting & Exhibition
||AI/Data Informatics: Computational Model Development, Validation, and Uncertainty Quantification
||Now On-Demand Only - Data-driven Approaches for Understanding Fatigue Damage Initiation
||Akhil Thomas, Ali Riza Durmaz, Chris Eberl, Harald Sack
|On-Site Speaker (Planned)
Understanding fatigue damage initiation is crucial for predicting lifetime of components, especially when loaded in high cycle regime. However, the mechanisms responsible for this phenomenon are less understood, primarily due to the experimental data scarcity that reveals them. Moreover, the available data is tough to be analyzed because of the following properties: multi-modality, incompleteness, and high-dimensionality.
We present a knowledge graph that reveals time evolution of initial fatigue damage. The data includes both microstructural and micromechanical features acquired/simulated prior to experiments. Different data-driven methods were employed for predicting grain-level damage initiation from these features. Graph-based or CNN-based approaches and combinations thereof were used to include and unify topological and morphological information. Conventional ML approaches were kept as a baseline. Including topological and morphological information was observed to be helpful for the task. Interpretability studies on the data-driven models revealed features that are relevant for prediction of initial fatigue damage.
||Machine Learning, Mechanical Properties, Iron and Steel