Iterative Learning Control of Direct Write Additive Manufacturing Using Online Process Monitoring

The spatial and dimensional errors that arise during fabrication using extrusion-based additive manufacturing (AM) methods like direct write printing inhibit manufacturing parts with increased geometric fidelity. Part fidelity can be improved by applying control strategies to correct geometric errors detected by directly measuring the material placement. This work presents a process monitoring and control strategy for AM that reduces the geometric errors in parts while they are fabricated. A laser scanner integrated into the AM system directly measures the deposited material in situ during fabrication, but not in real time, while the measurements are processed concurrently to determine the material's spatial placement and bead width errors online. Models relating the deposition process inputs to the resulting part geometry are combined with an Iterative Learning Control (ILC) algorithm to compensate for the measured geometric errors. The proposed strategy is implemented on a direct write printing system to monitor and control the bead width in 3D periodic functionally graded scaffolds. Here, the ILC algorithm uses the online measurements to learn the errors in the structure's repetitive elements as they are printed, then corrects the errors in subsequently fabricated elements. The experimental results show that the proposed process monitoring and control strategy reduced errors in the material bead width by 61–78% during scaffold fabrication.