Physics-informed Bayesian Learning Of Electrohydrodynamic Polymer Jet Printing Dynamics

This article discusses the development of a new end-to-end physics-informed Bayesian learning framework (GPJet) for self-calibrating electrohydrodynamics-based additive manufacturing (AM) technologies (E-jet printing). 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 and showed that the Machine Vision module can extract high-fidelity jet features in real-time from video data using an automated parallelized computer vision workflow. This work extends the application of intelligent AM machines to more complex working conditions while reducing cost and increasing computational efficiency.