| Abstract Scope |
Additive friction stir deposition is an emerging solid-state additive process leveraging rapid plastic deformation at elevated temperatures to enable location-specific material deposition. By producing fully-dense materials with forging-like mechanical characteristics, it has shown significant potential in a wide range of defense, automotive, and aerospace applications, including structural repair and dissimilar material cladding, as well as material recycling and upcycling. However, with fully-coupled heat and mass transfer and complex nature of the process, predicting the history of critical thermomechanical variables like strain rate, strain, and temperature based on the processing parameters remains difficult. To enable fast and precise prediction of thermomechanical variables, here we propose combining physics modeling with data-driven techniques, such as Bayesian learning. We show that compared to pure data-driven approaches, this combined strategy requires a much smaller dataset for training and cross validation, while enabling transfer learning. |