Enhancing the prediction quality of mechanical properties for powder bed fusion with laser beam by dynamic observation of flying particles

Abstract

Complex phenomena occur at the laser spot in powder bed fusion with laser beam (PBF-LB); thus, it creates several large process-parameter spaces such as power and scanning speed, along with many others. To allow for high-throughput parameter exploration, an efficient prediction method is necessary. To enhance the prediction quality of the mechanical properties, this paper proposes that the information collected from flying spatter particles, which are dominant in selective laser melting phenomena, can be used as feature values. Flying particles were dynamically observed using pulsed laser illumination and high-speed microscopy. Image treatment was used to detect both powder and droplet spatter, and it was possible to differentiate these two by assessing particle size—63 μm—which enables the quantification of each type. This approach was used at various laser powers and scanning speeds to characterize the single-bead shapes, porosity, and Vickers hardness for each parameter. The correlation between the counted amount of spatter and mechanical properties was investigated using regression analysis. The prediction accuracy of Vickers hardness using the volumetric energy density was observed to improve, with the coefficient of determination increasing from 0.172 to 0.539 when adding the amounts of powder and droplet spatter.

Keywords

• Powder bed fusion
• Dynamic observation
• Spatter
• Pulsed-laser illumination
• Regression