Abstract
Cooperative robotic additive manufacturing (C-RAM) offers the fabrication of large-scale components through the integration of multiple out-of-bounds robotic systems. However, factors such as wear and tear, calibration drift, temperature fluctuations, and vibrations impact the precision of the robots, leading to anomalies including dimensional inaccuracies, layer morphology issues, or mechanical defects. These anomalies adversely affect the strength and fatigue life of components. Ensuring CRAM systems are robust to defects is critical to the reliability and scalability of large-scale systems. We introduce a novel C-RAM system equipped with multi-fidelity multi-scale sensing (i.e., in-situ thermal imaging and laser scanning) to characterize process quality of a robotic fused deposition modeling (FDM) process. Control and data acquisition are designed through a ROS2-based digital twin modified for FDM fabrication control, allowing robust control of process parameters such as feed rate and extrusion multiplier. The system is evaluated via a case study on the effect of extruder temperature on material deposition height. Sensing data is analyzed, displaying high and low-fidelity quality indicators across extruder temperatures.
