| About this Abstract |
| Meeting |
MS&T22: Materials Science & Technology
|
| Symposium
|
Materials Informatics for Images and Multi-dimensional Datasets
|
| Presentation Title |
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 |
This work seeks to collect SEM, BSE, and Scanning White Light Interferometry (SWLI) images of Ti-6AL-4V fatigue fracture surfaces and apply Convolutional Neural Networks (CNNs) to identify high stress points, crack initiation sites, and predict values such as stress intensity factor and crack growth rate. SEM images are the standard for studying the topography of fracture surfaces, but BSE images and SWLI data offer the addition of compositional and surface height information as well. Computer Vision and Machine Learning were developed for optical images but have been successfully applied to electron images and a variety of other media. CNNs have been used to make successful classification and predictions of fracture surfaces. The images used to develop this model, creation of the CNNs, identified fatigue properties, and fracture characteristics will be presented. |