|About this Abstract
||MS&T22: Materials Science & Technology
||Additive Manufacturing Modeling, Simulation, and Machine Learning: Microstructure, Mechanics, and Process
||Studying Melt Pool Variation and Its Effects on the Formation of Porous Defects via GPU-based Process Simulation
||David Scott Anderson, Chaitanya Krishna Prasad Vallabh, Shawn Hinnebusch, Xiayun Zhao, Albert To
|On-Site Speaker (Planned)
||David Scott Anderson
The development of parts using Laser-Powder Bed Fusion (L-PBF) additive manufacturing processes face several challenges that stem from porosity formation. These porosities are typically generated from under-heating or over-heating the powders, also known as lack-of-fusion and keyholing, respectively. Although these two occurrences are closely linked to printing parameters such as laser scan speed, laser intensity, hatch spacing, and scan path orientation, the independent effects of each of these parameters on porosity formation is not well documented. Through GPU-based computational modeling, the effects of these process parameters on the melt pool geometry have been simulated. By comparing these simulations with serial cross-sectioning data of printed samples, these results aid in calibrating the simulations to predict melt pool geometries more accurately, that may lead to keyholing or lack-of-fusion, and thus porous defects.