About this Abstract |
Meeting |
2022 TMS Annual Meeting & Exhibition
|
Symposium
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Fatigue in Materials: Fundamentals, Multiscale Characterizations and Computational Modeling
|
Presentation Title |
Combining Multimodal Data of Fatigue Fracture Surfaces for Analysis in a CNN |
Author(s) |
Katelyn Jones, Elizabeth Holm, Anthony Rollett |
On-Site Speaker (Planned) |
Katelyn Jones |
Abstract Scope |
Machine learning and computer vision techniques can be used in materials science to improve and facilitate the analysis of microstructural data and images. Additionally, it can work on large amounts of data and diverse images when enough training data is provided. Convolutional neural networks (CNNs) are a key tool in making connections between fracture images, microstructure, and fatigue characteristics such as stress intensity factor, crack length, and load values. This project collects data from Ti-6Al-4V fracture surfaces in the form of BSE and SEM images, and height data from Scanning White Light Interferometry, and combines them to train a CNN and identify high stress points, crack initiation sites, and predict values such as stress intensity factor and plastic zone size. The images used to develop this model, creation of the CNNs, identified fatigue properties, and fracture characteristics will be presented. |
Proceedings Inclusion? |
Planned: |
Keywords |
Computational Materials Science & Engineering, Machine Learning, Titanium |