About this Abstract |
| Meeting |
2026 Annual International Solid Freeform Fabrication Symposium (SFF Symp 2026)
|
| Symposium
|
2026 Annual International Solid Freeform Fabrication Symposium (SFF Symp 2026)
|
| Presentation Title |
Real-Time Object Detection for Autonomous Robotic 3d Printing |
| Author(s) |
AGYA KWABENA AMPOMAH ABOAGYE-OTCHERE, Jianzhi Li, Ahmed Bendaouia |
| On-Site Speaker (Planned) |
AGYA KWABENA AMPOMAH ABOAGYE-OTCHERE |
| Abstract Scope |
Robotic 3d printing is a growing section within the additive manufacturing field. Robotics and AI in manufacturing have provided the platform to complete autonomy within these systems. This research aims to develop a robust computer vision system for real-time object detection to 3d print with a 6DOF robot in varying light conditions. The models are trained on custom datasets to perform defect detection and part identification. This vision-based data is mapped to the robot’s motion planning algorithm for autonomous adjustments. The implementation of the object detection system allows for precise monitoring of the printing process, enabling the robot to identify components and detect structural anomalies like warping and layer shifting. The model trained on all light conditions produced the best performance with 0.895 mAP50 and 0.880 mAP50-95. The best lighting-restricted model reached 0.830 mAP50 overall and slightly exceeded the baseline on the environmental subset alone with 0.936 mAP50 versus 0.924. |
| Proceedings Inclusion? |
Undecided |