| About this Abstract |
| Meeting |
MS&T21: Materials Science & Technology
|
| Symposium
|
Additive Manufacturing Modeling and Simulation: Microstructure, Mechanics, and Process
|
| Presentation Title |
Machine Learning – Assisted Navigation in the Additive Manufacturing Design Space |
| Author(s) |
Maher Alghalayini, Surya Kalidindi, Chris Paredis, Fadi Abdeljawad |
| On-Site Speaker (Planned) |
Maher Alghalayini |
| Abstract Scope |
Additive manufacturing (AM) has been the subject of active research in recent years. In laser powder bed AM, a laser beam is scanned across metal powder. Recent reports have shown considerable variability in the observable properties of the printed parts mainly due to AM process parameters. This significant variability makes it crucial for engineers to consider variability while quantifying the resultant properties. Here, we propose a sequential sampling design to drive the search towards processing parameters that are expected to yield optimal mechanical properties. The novelty of the proposed approach lies in its use of machine learning to consider both variability and uncertainty to propose the next search site and the sample size to be tested. Experimentally obtained AM process-property data of 316L steel are used to showcase our approach. This study is expected to yield a methodology to efficiently navigate the AM process parameter space towards optimal materials properties. |