Real-Time Optical Detection of Artificial Coating Defects in PBF-LB/P Using a Low-Cost Camera Solution and Convolutional Neural Networks - Victor Klamert; Timmo Achsel; Efecan Toker; Mugdim Bublin; Andreas Otto (Applied Sciences, 2023)
Additive manufacturing plays a decisive role in the field of industrial manufacturing in a wide range of application areas today. However, process monitoring, and especially the real-time detection of defects, is still an area where there is a lot of potential for improvement. High defect rates should be avoided in order to save costs and shorten product development times. Most of the time, effective process controls fail because of the given process parameters, such as high process temperatures in a laser-based powder bed fusion, or simply because of the very cost-intensive measuring equipment. This paper proposes a novel approach for the real-time and high-efficiency detection of coating defects on the powder bed surface during the powder bed fusion of polyamide (PBF-LB/P/PA12) by using a low-cost RGB camera system and image recognition via convolutional neural networks (CNN). The use of a CNN enables the automated detection and segmentation of objects by learning the spatial hierarchies of features from low to high-level patterns. Artificial coating defects were successfully induced in a reproducible and sustainable way via an experimental mechanical setup mounted on the coating blade, allowing the in-process simulation of particle drag, part shifting, and powder contamination. The intensity of the defect could be continuously varied using stepper motors. A low-cost camera was used to record several build processes with different part geometries. Installing the camera inside the machine allows the entire powder bed to be captured without distortion at the best possible angle for evaluation using CNN. After several training and tuning iterations of the custom CNN architecture, the accuracy, precision, and recall consistently reached >99%. Even defects that resembled the geometry of components were correctly classified. Subsequent gradient-weighted class activation mapping (Grad-CAM) analysis confirmed the classification results.