AFSD-Nets: A Physics-Informed Machine Learning Model for Predicting the Temperature Evolution During Additive Friction Stir Deposition
Tony Shi,
Jiajie Wu,
Mason Ma
et al.
Abstract:This study models the temperature evolution during additive friction stir deposition (AFSD) using machine learning. AFSD is a solid state additive manufacturing technology that deposits metal using plastic flow without melting. However, the ability to predict its performance using the underlying physics is in the early stage. A physics-informed machine learning approach, AFSD-Nets, is presented here to predict temperature profiles based on the combined effects of heat generation and heat transfer. The proposed… Show more
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.