| About this Abstract |
| Meeting |
2020 TMS Annual Meeting & Exhibition
|
| Symposium
|
ICME Gap Analysis in Materials Informatics: Databases, Machine Learning, and Data-Driven Design
|
| Presentation Title |
L-18 (Invited): Multi-fidelity Surrogate Assisted Framework for Prediction and Control of Meltpool Geometry in Additive Manufacturing Processes |
| Author(s) |
Sudeepta Mondal, Nandana Menon, Daniel Gwynn, Asok Ray, Amrita Basak |
| On-Site Speaker (Planned) |
Sudeepta Mondal |
| Abstract Scope |
Thermal gradients in the meltpool play an important role in determining the final microstructure during Additive Manufacturing (AM). Physics-based models predicting the thermal field during AM processes are often computationally expensive, and it becomes prohibitive to run adequate simulations for a grid search over the process parameter space in order to generate a look-up table for choosing desirable operating conditions. The problem becomes more critical in the presence of a hierarchy of multi-scale multi-physics models, where budget limitations constrain the number of queries from the more expensive higher fidelity models. In order to surmount the existing gap, we propose a multi-fidelity (MF) surrogate assisted framework that encapsulates the statistical information in the varied fidelity levels via MF Gaussian Processes and searches for optimal process parameters via Bayesian Optimization strategies aimed at reaching the optima with limited exploitation of higher fidelity models. |
| Proceedings Inclusion? |
Planned: Supplemental Proceedings volume |