Engineering (Aug 2021)
Selective Laser Melting under Variable Ambient Pressure: A Mesoscopic Model and Transport Phenomena
Abstract
Recent reports on the selective laser melting (SLM) process under a vacuum or low ambient pressure have shown fewer defects and better surface quality of the as-printed products. Although the physical process of SLM in a vacuum has been investigated by high-speed imaging, the underlying mechanisms governing the heat transfer and molten flow are still not well understood. Herein, we first developed a mesoscopic model of SLM under variable ambient pressure based on our recent laser-welding studies. We simulated the transport phenomena of SLM 316L stainless steel powders under atmospheric and 100 Pa ambient pressure. For typical process parameters (laser power: 200 W; scanning speed: 2 m∙s−1; powder diameter: 27 μm), the average surface temperature of the cavity approached 2800 K under atmospheric pressure, while it came close to 2300 K under 100 Pa pressure. More vigorous fluid flow (average speed: 4 m∙s−1) was observed under 100 Pa ambient pressure, because the pressure difference between the evaporation-induced surface pressure and the ambient pressure was relatively larger and drives the flow under lower pressure. It was also shown that there are periodical ripple flows (period: 14 μs) affecting the surface roughness of the as-printed track. Moreover, the molten flow was shown to be laminar because the Reynolds number is less than 400 and is far below the critical value of turbulence; thus, the viscous dissipation is significant. It was demonstrated that under a vacuum or lower ambient pressure, the ripple flow can be dissipated more easily by the viscous effect because the trajectory length of the ripple is longer; thus, the surface quality of the tracks is improved. To summarize, our model elucidates the physical mechanisms of the interesting transport phenomena that have been observed in independent experimental studies of the SLM process under variable ambient pressure, which could be a powerful tool for optimizing the SLM process in the future.
