Physics-Informed Bayesian learning of electrohydrodynamic polymer jet printing dynamics

Abstract

Calibration of highly dynamic multi-physics manufacturing processes such as electrohydrodynamics-based additive manufacturing (AM) technologies (E-jet printing) is still performed by labor-intensive trial-and-error practices. Such practices have hindered the broad adoption of these technologies, demanding a new paradigm of self-calibrating E-jet printing machines. Here we develop an end-to-end physics-informed Bayesian learning framework (GPJet) which can learn the jet process dynamics with minimum experimental cost. GPJet consists of three modules: the machine vision module, the physics-based modeling module, and the machine learning (ML) module. GPJet was tested on a virtual E-jet printing machine with in-process jet monitoring capabilities. Our results show that the Machine Vision module can extract high-fidelity jet features in real-time from video data using an automated parallelized computer vision workflow. The Machine Vision module, combined with the Physics-based modeling module, can also act as closed-loop sensory feedback to the Machine Learning module of high- and low-fidelity data. This work extends the application of intelligent AM machines to more complex working conditions while reducing cost and increasing computational efficiency.

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

“…Acknowledging the successful application of digitalization and real-time quality control in other industry ready AM technologies, research in MEW has started to shift the focus to process automation and in-situ process control 14 . With a camera vision-based system, process analysis is sped up using real-time process monitoring and image processing algorithms 10,15 . Identifying the Taylor cone area as an output parameter representing bre diameter, real-time process monitoring could analyse process stability and ultimately de ne the relationship between the jet angle, Taylor cone area, and bre diameter 14 .…”

“…The jet angle is de ned as being between the vertical line through the centre of the nozzle and the line that intersects the jet contact point with the collector. The jet angle dictates the accuracy of the material deposition, especially when the jet is deposited with changing directions 15 . The Taylor cone area is an output characteristic that reveals the information on printing stability but can also be correlated with bre diameter -the metric de ning the size of the printed bres 14 .…”