Towards Industry-Ready Additive Manufacturing: AI-Enabled Closed-Loop Control for 3D Melt Electrowriting

Abstract

Abstract Melt electrowriting (MEW) is an emerging high-resolution 3D printing technology applied in many fields including biomedical engineering, regenerative medicine, and soft robotics. The translation of the technology from academic labs to industry has been hampered by challenges such as timely experimentation, low printing throughput, poor reproducibility, and user-dependent printer operation. These issues arise because of the highly nonlinear and multiparametric nature of the MEW process. To address these challenges, we applied computer vision and machine learning (ML) to continuously monitor and analyse the process via real-time imaging, which is possible because the process uses a gap between the nozzle and collector. To collect data for training we developed an automated data collection methodology that eases the experimental time from days to hours. A feedforward neural network, working in concert with optimization methods and a feedback loop, is used to develop closed-loop control ensuring reproducibility of the printed parts. We demonstrate that machine learning allows streamlining the MEW operation via closed-loop control of the highly nonlinear 3D printing technology.