About this Abstract |
Meeting |
2023 TMS Annual Meeting & Exhibition
|
Symposium
|
Fatigue in Materials: Fundamentals, Multiscale Characterizations and Computational Modeling
|
Presentation Title |
Using Computer Vision to Identify Crack Initiation and Link to Fatigue Life |
Author(s) |
Katelyn Jones, Paul Shade, Reji John, William Musinski, Elizabeth Holm, Anthony Rollett |
On-Site Speaker (Planned) |
Katelyn Jones |
Abstract Scope |
This work seeks to collect SEM images of Ti-6AL-4V fatigue fracture surfaces and apply Convolutional Neural Networks (CNNs) to make a connection between fracture surfaces and fatigue life. SEM images of the crack initiation site, short crack region, and steady crack regions from varying fatigue life samples were taken at multiple magnifications to determine which length scale allows the machine learning algorithms to infer physically meaningful information. The images are compared using first unsupervised and then supervised machine learning methods to additionally determine which part of the fracture surface provides the information that links the fracture surface to the fatigue lifetime. The images taken, the algorithms used, identified fatigue properties, and fracture characteristics will be presented. |
Proceedings Inclusion? |
Planned: |
Keywords |
Computational Materials Science & Engineering, Other, Machine Learning |