Physics-added neural networks: An image-based deep learning for material printing system

Abstract

Drop-on-demand material printing condition optimization with empirical knowledge based on the trial-and-error method and implementation of computational fluid dynamics (CFD) requires a significant amount of time and costs. In addition, current state-of-the-art neural network algorithms can reduce such time and cost by predicting droplet formation over a temporal domain; however, insufficient data and physical inconsistencies across output results limit their extrapolative prediction performance. In this study, we performed multi-GPU-implemented CFD simulations to collect large-scale training data, and we propose a novel algorithm for embedding physics knowledge in neural networks: physics-added neural networks. Integrating an image-to-image deep learning algorithm with our novel physics-embedding module, we obtain more consistent physical characteristics of the material printing process, such as mass and energy. Furthermore, by guiding the neural network to adhere to mass and energy conservation laws, the possible solution space of neural network training is restricted, which enhances convergence stability and decreases training time.