Authors:
Lennart Waalkes (Fraunhofer Research Institution for Additive Manufacturing Technologies IAPT, Germany)
Johannes Helmholz (Fraunhofer Research Institution for Additive Manufacturing Technologies IAPT, Germany)
Anil Ölmez (Institute for Industrialization of Smart Materials ISM, Hamburg University of Technology TUHH, Germany)
Matthias Brück (Fraunhofer Research Institution for Additive Manufacturing Technologies IAPT, Germany)
Abstract:
Metal Binder Jetting is a promising additive manufacturing technology, but powder spreading defects can compromise part quality and cause process interruptions. This study presents a Deep Learning pipeline leveraging synthetic data to detect such defects in real time. For training this pipeline, a total of 1,107 real process images are used. First, a pixel-diffusion UNet2D model generates synthetic images, which are filtered using a VQ-VAE model to ensure realistic representations. To automatically label synthetic data classified as realistic, a Faster R-CNN model is trained, eliminating the need for manual labelling. Finally, a YOLO11 model, trained only on synthetic data set, is tested for in-situ defect detection. It could be shown that the YOLO11 model achieves a mean Intersection over Union value above 0.6, successfully detecting two pre-defined powder spreading defects. This approach significantly reduces the time and cost of data collection while enabling robust, real-time defect detection.
DOI:
https://doi.org/10.59499/EP256720061
