About this Abstract |
| Meeting |
MS&T25: Materials Science & Technology
|
| Symposium
|
Additive Manufacturing Modeling, Simulation, and Machine Learning: Microstructure, Mechanics, and Process
|
| Presentation Title |
Enhancing Additive Manufacturing Process Parameter Design by Computational Fluid Dynamics |
| Author(s) |
Adriana Eres-Castellanos, Tomas Scuseria, Omar Mireles, Zach Jibben, Nathan Peterson, Lindsay O'Brien, Cheryl L. Hawk, Amber N. Black, Amy Clarke |
| On-Site Speaker (Planned) |
Adriana Eres-Castellanos |
| Abstract Scope |
Additive manufacturing (AM) encompasses a variety of processes that enable the building of complex geometries while minimizing material waste through the sequential deposition of layers of melted and solidified metal. Successfully using these techniques requires process parameter optimization, often done through a combination of the operator’s experience and resource-consuming experimental parametric studies. To minimize experimental efforts, research has focused on developing computational models that enable the prediction of defect formation and microstructure evolution. One of the most powerful tools in this context is computational fluid dynamics (CFD), which incorporates a wide range of physical models describing heat transfer, fluid flow and phase transitions, among others, enabling the prediction of melt pool dynamics and solidification behavior. In this talk, we will discuss different CFD models of a variety of AM processes, and how experimental validation conducted by simple geometry experiments can be used to inform process parameter design for complex geometries. |