Additive Manufacturing (AM), also known as 3D printing, is a disruptive manufacturing technology that has grown rapidly in the manufacturing industry and has gained a lot of attention owing to its ability to manufacture parts with complex features by using a layer-by-layer approach. The variability in the final product quality is however one of the major hurdles to the widespread application of such techniques in production environment. In particular, the mechanical properties and the quality of the manufactured part largely depend on the distribution of temperature fields during the AM process. Numerical simulations, such as finite element analyses, are commonly used to simulate the thermal history during the AM process, but they are known to be expensive and time-consuming, and hence they cannot be used in real-time. In this context, we propose a machine learning approach for the fast and accurate prediction of the thermal field evolution. Our approach consists of a variational autoencoder that encodes the thermal fields into a latent space, combined with a recurrent neural network that simulates the temporal process in the latent space. In order to generate the datasets for training and testing this model, 256 finite element-based
thermal simulations were performed using Cenaero’s virtual manufacturing software, Morfeo.