| About this Abstract |
| Meeting |
Materials Science & Technology 2020
|
| Symposium
|
Additive Manufacturing Modeling and Simulation: AM Materials, Processes, and Mechanics
|
| Presentation Title |
Multi-Fidelity Surrogate Assisted Prediction of Melt Pool Geometry in Additive Manufacturing |
| Author(s) |
Nandana Menon, Sudeepta Mondal, Daniel Gwynn, Amrita Basak |
| On-Site Speaker (Planned) |
Nandana Menon |
| Abstract Scope |
Melt pool geometry plays a critical role in controlling the microstructure and therefore the properties of additively manufactured components. There is a plethora of models available for predicting the steady state melt pool geometry in metal additive manufacturing. There models are either fast but inaccurate (e.g., analytical models) or slow and accurate (e.g., finite element-based models). The objective of this paper is to take advantage of the hierarchy of multi-scale multi-physics models to construct a multi-fidelity (MF) surrogate assisted framework that encapsulates the statistical information in the varied fidelity levels via MF Gaussian Processes with computational budget constraint so that the computationally inexpensive models are exploited more and the usage of expensive models are restricted. We demonstrate this framework for a nickel-base superalloy, CMSX-4® using three different fidelity models such as Eagar-Tsai, analytical directed energy deposition (DED) model, and Autodesk NetFabb DED model. |