In this work a finite-element framework for the numerical simulation of the heat transfer analysis of additive manufacturing processes by powder-bed technologies, such as Selective Laser Melting, is presented. These kind of technologies allow for a layer-by-layer metal deposition process to cost-effectively create, directly from a CAD model, complex functional parts such as turbine blades, fuel injectors, heat exchangers, medical implants, among others. The numerical model proposed accounts for different heat dissipation mechanisms through the surrounding environment and is supplemented by a finite-element activation strategy, based on the born-dead elements technique, to follow the growth of the geometry driven by the metal deposition process, in such a way that the same scanning pattern sent to the numerical control system of the AM machine is used. An experimental campaign has been carried out at the Monash Centre for Additive Manufacturing using an EOSINT-M280 machine where it was possible to fabricate different benchmark geometries, as well as to record the temperature measurements at different thermocouple locations. The experiment consisted in the simultaneous printing of two walls with a total deposition volume of 107 cm3 in 992 layers and about 33,500 s build time. A large number of numerical simulations have been carried out to calibrate the thermal FE framework in terms of the thermophysical properties of both solid and powder materials and suitable boundary conditions. Furthermore, the large size of the experiment motivated the investigation of two different model reduction strategies: exclusion of the powder-bed from the computational domain and simplified scanning strategies. All these methods are analysed in terms of accuracy, computational effort and suitable applications.Peer ReviewedPostprint (author's final draft

Summary This work introduces an innovative parallel fully‐distributed finite element framework for growing geometries and its application to metal additive manufacturing. It is well known that virtual part design and qualification in additive manufacturing requires highly accurate multiscale and multiphysics analyses. Only high performance computing tools are able to handle such complexity in time frames compatible with time‐to‐market. However, efficiency, without loss of accuracy, has rarely held the centre stage in the numerical community. Here, in contrast, the framework is designed to adequately exploit the resources of high‐end distributed‐memory machines. It is grounded on three building blocks: (1) hierarchical adaptive mesh refinement with octree‐based meshes; (2) a parallel strategy to model the growth of the geometry; and (3) state‐of‐the‐art parallel iterative linear solvers. Computational experiments consider the heat transfer analysis at the part scale of the printing process by powder‐bed technologies. After verification against a three‐dimensional (3D) benchmark, a strong‐scaling analysis assesses performance and identifies major sources of parallel overhead. A third numerical example examines the efficiency and robustness of (2) in a curved 3D shape. Unprecedented parallelism and scalability were achieved in this work. Hence, this framework contributes to take on higher complexity and/or accuracy, not only of part‐scale simulations of metal or polymer additive manufacturing but also in welding, sedimentation, atherosclerosis, or any other physical problem where the physical domain of interest grows in time.

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