individual
What campus are you from?
Daytona Beach
Authors' Class Standing
Anthony Seholm, Graduate Student
Lead Presenter's Name
Anthony Seholm
Faculty Mentor Name
Dr. Yue Zhou
Abstract
Directed Energy Deposition (DED) Additive Manufacturing of stainless steel is a promising method for the fabrication/repair of metallic structures. Understanding melt pool dynamics in DED is essential for achieving geometric stability and material integrity. However, the process involves complex physical variations (e.g., powder flow, laser energy absorption, and melt pool fluid behavior), which remain difficult to quantify through experiments alone. To address these challenges, this study establishes a multi-scale modeling framework integrating Discrete Element Method (DEM) and Computational Fluid Dynamics (CFD) to elucidate the underlying physical mechanisms governing melt pool dynamics. The work is divided into two phases. The first phase focuses on a particle-laser interaction simulation that models powder delivery from two coaxial annular nozzles and obtains particle catchment efficiency, landing positions, and laser-powder energy absorption. Preliminary results show an increase in nozzle standoff can increase catchment efficiency and reduce radial spread, whereas increasing carrier-gas velocity similarly increases catchment efficiency, but increases radial spread. Catchment efficiency falls between 30% to 40% depending on the input parameters, and absorbed laser energy is between 4% and 5%, with both results being supported by literature. The second phase will investigate melt pool dynamics, including transient thermal variations and fluid flow behavior. Outcomes from the first phase directly inform the subsequent melt pool simulation, which will be validated through DED experiments with in-situ high-speed imaging and pyrometry. This integrated DEM-CFD experiment approach establishes a predictive foundation for optimizing DED process parameters and improving the quality of DED additive manufacturing.
Did this research project receive funding support from the Office of Undergraduate Research.
No
DEM Modeling of Powder Capture and Energy Absorption in DED Additive Manufacturing of SS 316L
Directed Energy Deposition (DED) Additive Manufacturing of stainless steel is a promising method for the fabrication/repair of metallic structures. Understanding melt pool dynamics in DED is essential for achieving geometric stability and material integrity. However, the process involves complex physical variations (e.g., powder flow, laser energy absorption, and melt pool fluid behavior), which remain difficult to quantify through experiments alone. To address these challenges, this study establishes a multi-scale modeling framework integrating Discrete Element Method (DEM) and Computational Fluid Dynamics (CFD) to elucidate the underlying physical mechanisms governing melt pool dynamics. The work is divided into two phases. The first phase focuses on a particle-laser interaction simulation that models powder delivery from two coaxial annular nozzles and obtains particle catchment efficiency, landing positions, and laser-powder energy absorption. Preliminary results show an increase in nozzle standoff can increase catchment efficiency and reduce radial spread, whereas increasing carrier-gas velocity similarly increases catchment efficiency, but increases radial spread. Catchment efficiency falls between 30% to 40% depending on the input parameters, and absorbed laser energy is between 4% and 5%, with both results being supported by literature. The second phase will investigate melt pool dynamics, including transient thermal variations and fluid flow behavior. Outcomes from the first phase directly inform the subsequent melt pool simulation, which will be validated through DED experiments with in-situ high-speed imaging and pyrometry. This integrated DEM-CFD experiment approach establishes a predictive foundation for optimizing DED process parameters and improving the quality of DED additive manufacturing.