About this Abstract |
Meeting |
Materials Science & Technology 2020
|
Symposium
|
Materials Informatics for Images and Multi-dimensional Datasets
|
Presentation Title |
Identifying Crack Initiation Sites with CNNs |
Author(s) |
Katelyn Jones, Elizabeth Holm, Anthony Rollett |
On-Site Speaker (Planned) |
Katelyn Jones |
Abstract Scope |
The application of machine learning techniques in materials science has allowed for a greater understanding of microstructure and more efficient analysis of large amounts of data. Convolutional neural networks (CNNs) have been used with images to make connections between microstructure, stress state, and fatigue life. This project uses CNNs on a combination of experimental and simulated image data to identify high stress points that can initiate cracks and cause fatigue failure. We focus on aerospace materials such as alloy 718, which will be studied because of its applicability for high temperature service and cyclic loading. These results will be used to create a model to locate the crack sites before they form and predict the causes of failure and life of future parts. The application of CNNs in this instance, simulations used, and identified causes of crack initiation will be presented. |