| About this Abstract |
| Meeting |
MS&T21: Materials Science & Technology
|
| Symposium
|
Materials Informatics for Images and Multi-dimensional Datasets
|
| Presentation Title |
Building a Database of Fatigue Fracture Images to train a CNN |
| Author(s) |
Katelyn Jones, Paul Shade, William Musinski, Reji John, Adam Pilchak, Anthony Rollett, Elizabeth Holm |
| 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 images from a variety of Ti-6Al-4V fracture surfaces to create a database to train a CNN and identify high stress points, crack initiation sites, and predict values such as stress intensity factor. The images used to develop this model, creation of the CNNs, identified fatigue properties, and fracture characteristics will be presented. |