| Abstract Scope |
During the Laser Powder Bed Fusion (LPBF) additive manufacturing (AM) process, various forces, such as vapor recoil, surface tension, Marangoni, gravity, and buoyancy, interact in complex ways, significantly impacting melt pool stability and metallic spatter behavior. Despite their critical role, controlling and predicting spatter formation remains a major challenge due to the simultaneous physical processes involved. To better understand and mitigate these issues, two complementary approaches were developed. First, a high-speed camera system was used to record the LPBF process, with a custom Python algorithm developed to analyze video frames. This tool automatically identifies and tracks spatter characteristics, amount, size, ejection speed and angle, and evaluates melt pool size and stability. A melt pool stability index was proposed, quantifying process consistency through melt pool length variation. A high stability index correlates with fewer spatters and a more stable melt pool, leading to higher-quality parts. Second, a novel, dimensionless spatter index was introduced to quantify the synthetic influence of vapor recoil and surface tension forces on spatter formation. This index is derived using an analytical model based on calculated temperature fields and alloy-specific properties, tested across multiple alloys and process conditions. The spatter index exhibits a clear linear relationship with both spatter amount and ejection speed, offering insight into formation mechanisms. Additionally, a process map based on this spatter index was created for the AF9629 alloy to optimize process conditions. Together, the image-based stability index and force-driven spatter index provide powerful, complementary tools for diagnosing and improving LPBF process. |