About this Abstract |
Meeting |
2023 TMS Annual Meeting & Exhibition
|
Symposium
|
AI/Data Informatics: Computational Model Development, Validation, and Uncertainty Quantification
|
Presentation Title |
M-7: How Can I Use Machine Learning to Predict all the Process Parameters that will lead to a Specific Material Property in my Advanced Manufacturing Process? |
Author(s) |
Lizzy Coda, Loc Truong, Colby Wight, WoongJo Choi, Tegan Emerson, Keerti Kappagantula, Henry Kvinge |
On-Site Speaker (Planned) |
Lizzy Coda |
Abstract Scope |
The relationship between process parameters and properties in advanced manufacturing is often many-to-one; there are many different process parameters that can yield the same property combination in a component. While standard deep learning architectures are well-adapted to predicting properties given process conditions (learning in one direction), the task of predicting process parameters that will yield a single material property combination is significantly more challenging. However, being able to efficiently and systematically solve this problem in the absence of large amounts of data would be a significant benefit to the materials and manufacturing community since this addresses experimental design and process optimization. In this presentation, we describe how a recent deep learning-based approach to this problem, Bundle Networks, can be adapted to better model this challenging problem. |
Proceedings Inclusion? |
Planned: |