| About this Abstract |
| Meeting |
2020 TMS Annual Meeting & Exhibition
|
| Symposium
|
Characterization: Structural Descriptors, Data-Intensive Techniques, and Uncertainty Quantification
|
| Presentation Title |
Predicting Microstructure-sensitive Fatigue-crack Path in 3D Using a Machine Learning Framework |
| Author(s) |
Kyle Pierson, Aowabin Rahman, Ashley Spear |
| On-Site Speaker (Planned) |
Aowabin Rahman |
| Abstract Scope |
Service lives of structural components are often significantly influenced by initiation and evolution of microstructure-sensitive fatigue cracks; however, the dependence of crack propagation on microstructural features can be complex and difficult to model. In this talk, we present a convolutional neural network (CNN)-based framework to approximate the underlying function relating crack path to the relevant microstructural and micromechanical features. The key components of the framework include: (i) a feature-selection scheme to determine a lower-dimensional representation of spatially varying features; (ii) a CNN model to compute the local kink angle of the crack using two different parameterization strategies; and (iii) a dropout technique to compute the model uncertainties associated with the CNN predictions. In general, the CNN model performs comparatively better than other ML algorithms in predicting crack path – even when micromechanical fields are not available as inputs, as the CNN can account for the spatial distribution of microstructural features |
| Proceedings Inclusion? |
Planned: Supplemental Proceedings volume |