A Physics-Informed Neural Network Modeling Approach to Direct Ink Writing 3D Printing Process

Direct ink writing is a 3D printing process and the quality of this process depends on the steady-state flow of materials at the tip of nozzle. In this paper, we investigate a data-driven approach, the physics informed neural network, for predicting flows, and compare different neural network architectures and their performance.