| About this Abstract |
| Meeting |
Materials Science & Technology 2020
|
| Symposium
|
Materials Design through AI Composition and Process Optimization
|
| Presentation Title |
Enabling Process Optimization Using High-throughput Machine Learning-based Image Analysis |
| Author(s) |
Tiberiu Stan, Peter Voorhees |
| On-Site Speaker (Planned) |
Tiberiu Stan |
| Abstract Scope |
One of the advantages of modern materials processing techniques (such as additive manufacturing) is the ability to correct for defects during part fabrication. To be successful, the images obtained through in-situ monitoring must be rapidly collected and analyzed. We showcase the use of convolutional neural networks (CNNs) as an efficient way to accurately evaluate large materials imaging datasets. Novel approaches to CNN training using experimental and synthetic datasets will be presented, as well as techniques for comparing and determining the success of different machine learning methods. Future steps to incorporating artificial intelligence into process optimization and anomaly detection will also be discussed. |