| About this Abstract |
| Meeting |
MS&T23: Materials Science & Technology
|
| Symposium
|
Materials Informatics for Images and Multi-dimensional Datasets
|
| Presentation Title |
Using Computer Vision to Cluster Fatigue Life Based on Small Crack Characteristics |
| Author(s) |
Katelyn Jones, Paul Shade, Reji John, 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 samples of varying fatigue life and stress levels 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. |