Enhancing In-Situ Monitoring of Cooperative 3D Printing via Edge Detection and Image Augmentation

Abstract Cooperative 3D printing (C3DP) is an emerging field in Swarm Manufacturing (SM) that allows larger format objects to be printed in parallel for improved efficiency without compromising print quality. Our previous work in C3DP has presented a real-time process monitoring framework for C3DP, capable of detecting defects in fused deposition modeling (FDM) 3D printing. However, the annotation accuracy and image-matching scores in side-view error detection are too low to achieve effective closed-loop control. In this study, we improve the in-situ monitoring framework by integrating the Canny edge detector for enhanced image-matching accuracy. Also, we retrained the computer vision model based on YOLOv8 with over 7000 images utilizing image argumentation, achieving an increase of 25% compared to the 276 images used initially. With this improvement, the system has gained robustness, adaptability, and reliability across diverse camera perspectives and error conditions in real-world applications. Therefore, this research will show the improvement from the original model with a side-by-side in-situ analysis, demonstrating the increased effectiveness of real-time detection and adjustment in cooperative 3D printing. The results will highlight the potential of this enhanced system to be adapted across various applications in the additive manufacturing industry, ultimately moving toward autonomous, high-precision 3D printing systems in complex manufacturing environments.