Multi-layer thermal history prediction framework for directed energy deposition based on extended physics-informed neural networks (XPINN)

This paper presents an eXtended physics-informed neural networks (XPINN)-based framework for predicting the temperature history during a multi-layer Directed Energy Deposition (DED) process. The proposed XPINN-based framework, advancing from its PINN-based counterpart, demonstrates significant accuracy improvement, around 50 % reduction in RMSE and maximum absolute error, and extended capability of temperature history prediction with domain decomposition for more complex configurations such as interpass time, void, and interruption of scan that are prevalent in real-life DED designs. It is validated via a series of 2D benchmark tests against numerical simulations with an increasing degree of complexity. The effect of different domain decompositions is compared and discussed. Strategies that improve the training outcome are also proposed and analysed. With the enhanced capability of working on more complex configurations while retaining the characteristic availability of derivative information, the proposed framework brings process-ware design optimisation based on scientific machine learning (SciML) techniques one step closer to the application to real-life additive manufacturing applications.