Physics-informed neural networks to accelerate heat transfer predictions in additive manufacturing

Accurate and scalable prediction of thermal history is pivotal for optimizing print quality in extrusion deposition additive manufacturing (EDAM). This paper investigates the applicability of physics-informed neural networks (PINNs) for 3D heat transfer analysis during the additive manufacturing process. As finite element method (FEM) requires increasingly fine meshes and smaller time increments to achieve higher accuracy, its computational cost rises substantially. PINNs offer a more scalable solution by eliminating the need for meshes and time increment schemes. We achieve this by recasting the solution to the differential equation as a stochastic minimization problem. While the current focus is a single forward problem, the paper sets the groundwork for future research into parametric problems, where PINNs demonstrate their full potential by efficiently solving a range of scenarios under varying initial, boundary conditions, and material properties. Our results show that PINNs can be used to solve the 3D heat transfer equation on evolving geometries without the need for spatial discretization and time-stepping schemes. This study showcases the growing relevance of PINNs in manufacturing simulations, with future implications for real-time control and optimization in advanced manufacturing.