| About this Abstract |
| Meeting |
MS&T22: Materials Science & Technology
|
| Symposium
|
Materials Informatics for Images and Multi-dimensional Datasets
|
| Presentation Title |
Combining Limited Image and Tabular Data to Understand Failure Modes in Metals |
| Author(s) |
Jonathan R. Owens, Andrew Detor, Jason Parolini, Daniel Ruscitto |
| On-Site Speaker (Planned) |
Jonathan R. Owens |
| Abstract Scope |
The "small data" problem appears frequently in industrial applications of machine learning. This relative dearth of data makes the automated extraction of meaningful microstructural properties from images and tabular data difficult, as pre-trained models are typically too far out-of-distribution for specific uses cases. In this talk, we explore various methods to combine tabular and image data to understand failure modes in metals via fracture surface analysis, including classical and deep-learning-based approaches. We examine methods such as support-vector-machines, UNet, and Mask-RCNN to classify the failure mode as a function of an input image and tabular data. |