A High-speed Multimodal In-situ Monitoring System For Detecting Porosity And Deformation Defects In The Powder Bed Fusion Of Metallic Parts

Porosity and deformation defects remain critical challenges to consistent, high-quality production of metallic parts using powder bed fusion (PBF). Existing state-of-the-art monitoring systems typically lack the speed required for real-time adjustments and often target only single defect types. To address these limitations, we present a high-speed, multi-modal in-situ monitoring system operating at up to 20 kHz. The system integrates dynamic region-of-interest cameras in both the visible and short-wave infrared ranges with a GPU-optimized, machine learning-based image processing pipeline. By incorporating local print context, the system accurately identifies porosity and deformation defects, enabling rapid corrective actions. Validation on 316L stainless steel samples, deliberately engineered to exhibit these defects, demonstrates Pearson correlation coefficients of 0.9493 for porosity prediction and normalized mean absolute errors of 17% for deformation detection. The results show promise for the system to be effectively used for high-speed, intra-layer closed-loop control, significantly improving the PBF process.