Combining High-Throughput Imaging in Visible and SWIR wavelengths for In-Situ Porosity Prediction in Laser Powder Bed Fusion

Laser powder bed fusion is at the forefront of manufacturing metallic objects, particularly those with complex geometries or those produced in limited quantities. However, this 3D printing method is susceptible to several printing defects due to the complexities of using a high-power laser with ultra-fast actuation. Accurate online print defect detection is therefore in high demand, and this defect detection must maintain a low computational profile to enable low-latency process intervention. In this work, we propose a low-latency LPBF defect detection algorithm based on fusion of images from high-speed cameras in the visible and short-wave infrared (SWIR) spectrum ranges. First, we design an experiment to print an object while both imposing porosity defects on the print, and recording the laser's melt pool with the high-speed cameras. We then train variational autoencoders on images from both cameras to extract and fuse two sets of corresponding features. The melt pool recordings are then annotated with pore densities extracted from the printed object's CT scan. These annotations are then used to train and evaluate the ability of a fast neural network model to predict the occurrence of porosity from the fused features. We compare the prediction performance of our sensor fused model with models trained on image features from each camera separately. We observe that the SWIR imaging is sensitive to keyhole porosity while the visible-range optical camera is sensitive to lack-of-fusion porosity. By fusing features from both cameras, we are able to accurately predict both pore types, thus outperforming both single camera systems.