A scalable parallel finite element framework for growing geometries. Application to metal additive manufacturing

Abstract

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.

Fast Detection of Heat Accumulation in Powder Bed Fusion Using Computationally Efficient Thermal Models

“…It was shown that this modeling method can correctly capture the thermal history as obtained using the thermocouples. More importantly, it was observed by Neiva et al [ 29 ] that the model correctly predicts the high temperatures measured near an overhanging feature. Therefore, it is deemed suitable for the intended purpose of identifying geometric features which cause local heat accumulation during the LPBF process.…”

“…It is based on the thermal model previously presented by Chiumenti et al [ 27 ] for simulating material deposition in the laser material deposition (LMD) process. The same concept was later used for LPBF modeling in several publications [ 24 , 28 , 29 ]. The experimental validation of the model has been presented in Davies [ 24 ] where simulation results were compared with temperatures empirically recorded using in situ thermocouples placed inside the part during the LPBF process.…”

A computational phase transformation model for selective laser melting processes

“…The mechanical parameters, i.e. Poisson's ratio ν, Young's modulus E and yield limit σ y , are taken from the literature, see [32,36,46]. The hardening modulus H sol is, at this stage, chosen without particular literature reference.…”

“…The thermal material parameters, i.e. the expansion coefficient α, the specific heat capacity c, the conductivity k and the latent heat L, as well as the initial temperature θ ini and the reference temperature θ ref are parameters based on [32,36]. The respective effective counterparts which are used to calculate the material response follow from the homogenisation approach introduced in the previous chapters in contrast to material models which directly incorporate temperature-dependent averaged material properties.…”

Element activation method and non-conformal dynamic remeshing strategy to model additive manufacturing

“…A lot of past AM simulations involve very small deposits or have approximated the process due to the high cost of the simulations. Due to this fact more recent articles have started using advanced numerical methods to allow for less costly simulations, this includes multi-scale approaches [8] and remeshing approaches [9] [10].…”