An adaptive Finite Element strategy for the numerical simulation of additive manufacturing processes
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In this work an adaptive Finite Element strategy to deal with the numerical simulation of Additive Manufacturing (AM) processes is presented. The Selective Laser Melting (SLM) is chosen as the reference technology because of its great diffusion in the industrial manufacturing chain, although the proposed methodology can be applied to the numerical simulation of all types of AM. An octree-based mesh adaptivity approach is adopted allowing for the use of much finer meshes within the processing zone, the so called Thermo-Mechanically Affected Zone (TMAZ), if compared to the rest of the computational domain. Although the adaptive meshing is vital to keep controlled the computational resources through the entire simulation of the fabrication process, the accuracy of the results can be compromised by the coarsening strategy, and particularly when simulating the SLM process, where the mesh size can vary from microns (TMAZ) to centimetres (close to the build-plate). This loss of accuracy can spoil the original efforts in refining the mesh in the process zone. Therefore a strategy to compensate for information loss in the adaptive refinement simulation of additive manufacturing processes is developed. The main idea is to add two correction terms which compensate for the loss of accuracy in the coarsening process of the mesh in the already manufactured regions. The proposed correction terms can be interpreted as a Variational Multiscale enhancement on the adaptive mesh. This allows one to successfully simulate the additive manufacturing process by using an adaptively coarsened mesh, with results which have an accuracy very similar to the one of a uniformly refined mesh simulation, at a fraction of the computational cost. Numerical examples illustrate the performance of the proposed strategy.
CitationBaiges, J. [et al.]. An adaptive Finite Element strategy for the numerical simulation of additive manufacturing processes. "Additive manufacturing", 2021, vol. 37, p. 101650:1-101650:13.
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