4-year SNF-Innosuisse funded Bridge Discovery grant for Robotic 3D Printing
The highly competitive grant has been awarded to Prof. Dr. Alisa Rupenyan at ZHAW CAI, Dr. Efe Balta at inspire AG, and Prof. Dr. John Lygeros at the Automatic Control Laboratory, ETH Zurich. They will collaborate to establish innovative technology for polymer 3D printing control and optimization.
A highly competitive Bridge Discovery grant of 2 Mln CHF has been shared between Prof. Dr. Alisa Rupenyan at ZHAW CAI, Dr. Efe Balta at inspire AG, and Prof. Dr. John Lygeros at the Automatic Control Laboratory , ETH Zurich. The three groups will collaborate in the next four years to establish innovative technology for polymer 3D printing extrusion head, that enables the use of existing equipment with minimal intervention, and provides intelligent software for control and optimization of the printing process and quality.
Current additive manufacturing systems operate open-loop without real-time sensing, resulting in poor reliability and repeatability that limits industrial adoption. Existing robotic polymer printing solutions rely on ad-hoc calibration without closed-loop optimization or integrated trajectory control. This Bridge Discovery project develops a comprehensive solution combining an integrated smart extrusion tool head with novel software for process planning, monitoring, and control. The approach integrates print and robot trajectory planning across three technical focus areas:
- Hardware & Sensing: Tool head capable of printing common and technical polymers with minimal maintenance and comprehensive sensing;
- Optimization & Control: Novel closed-loop control methods for process control and print path optimization;
- Robotics & Integration: Coordination and implementation for industrial robot manipulators.
The proposed solution will deliver material and platform-independent hardware/software modules with integrated sensors, computation capability for closed-loop extrusion control, efficient communication interfaces, and autonomous system identification. The software tools will provide parameter optimization and combined robot-extruder toolpath optimization using part design inputs.
The IAI group at ZHAW CAI (Prof. Dr. Alisa Rupenyan) is responsible for the robotics integration of the new technology. This grant follows from joint extensive research on additive manufacturing control and optimization with promising results.
