Electromagnetic Scattering Properties of Metal Powder Cloud for Laser Powder Bed Fusion (LPBF) Additive Manufacturing (AM)

Laser powder bed fusion (LPBF) is a cost-effective and relatively fast additive manufacturing (AM) technique, utilizing a laser source to selectively melt metallic powder and produce objects with intricate geometries. Nevertheless, establishing effective real-time monitoring methods for this process remains a notable challenge. The interaction between the laser and metallic powder leads to the ejection of particles, referred to as spatter, with varying sizes, velocities, trajectories, and spatial distributions. Literature indicates that the behavior of these spatters can serve as a potential indicator of defect generation during the AM process. This study investigates the potential for employing a well-established electromagnetic (EM) model to monitor the scattering properties of spatters. This approach serves as a potential tool to identify variations in spattering behavior that might be associated with defect generation during the process. The study explores how parameters, such as spatial distribution and the number of particles (in a given volume), impact the scattering properties, accuracy, and efficiency of the method. Changes in spattering spatial distribution resulting from variations in processing parameters, including laser power, scan speed, and chamber pressure, were investigated. Some of these conditions resulted in the formation of a deep keyhole zone. The results demonstrated that monitoring radar cross-section (RCS) of the spatters could serve as a metric to distinguish between conditions that lead to a deeper keyhole and those that either do not create a keyhole zone or result in a shallower keyhole. Additionally, the concept of the “sphere-of-influence (SoI)” is used as a tool for improving computational efficiency and potential use in method validation. The insights from this study have the potential to contribute to the advancement of real-time LPBF monitoring, presenting possibilities for improved quality control and defect detection in AM processes.