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An accurate, adaptive and scalable parallel finite element framework for the part-scale thermo-mechanical analysis in metal additive manufacturing processes

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Abstract

This work introduces a distributed memory machine and octree-based Finite Element (FE) framework for modeling metal Additive Manufacturing (AM) processes. In this sense, an Adaptive Mesh Refinement (AMR) is used to accurately capture the complex geometrical domain and the physical phenomena of AM processes for the part-scale analysis. AMR is used in conjunction with refinement criteria suitable for this problem: (1) a geometric criterion for refining the material deposition path, and (2) an accuracy criterion based on an a-posteriori error-indicator of the displacement field. As a consequence, the geometry and numerical solution are accurately captured whilst the number of FEs is kept controlled along the simulation. Numerical simulations involving growing domains are presented to assess the accuracy and computational efficiency of the framework with different octree-based and structured fixed mesh configurations. The overall performance of the framework is evaluated through a strong scalability analysis to track the evolution of the computational time along the simulation, where all steps composing the AM pipeline are independently analyzed. The strong scalability test consists of a discrete evolving domain with over 15 M active nodes, solved on a High-Performance Computer (HPC) using 2, 048 processors.

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Acknowledgements

The financial support from the Spanish Ministry of Economy and Competitiveness, through the Severo Ochoa Programme for Centres of Excellence in R &D (CEX2018-000797-S), is gratefully acknowledged. This work has been supported by the European Union’s horizon 2020 research and innovation programme (H2020-DT-2019-1 No. 872570) under the KYKLOS 4.0 Project (An Advanced Circular and Agile Manufacturing Ecosystem based on rapid reconfigurable manufacturing process and individualized consumer preferences) and by the Ministry of Science, Innovation and Universities (MCIU) via: the PriMuS project (Printing pattern based and MultiScale enhanced performance analysis of advanced Additive Manufacturing components, ref. num. PID2020-115575RB-I00). The strong-scaling simulations were done on the LUMI supercomputer at Kajaani, Finland. The authors acknowledge PRACE for granting access to the resources of LUMI via Project EHPC-DEV-2022D12-013. The support of the Red Española de Supercomputación (RES) and the European PRACE network is acknowledged. The authors gratefully acknowledge ArcelorMittal for their valuable contributions and support in this research.

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Authors and Affiliations

  1. International Center for Numerical Methods in Engineering (CIMNE), Edificio C1, Campus Norte, Gran Capitán s/n, 08034, Barcelona, Spain

    Carlos A. Moreira, Manuel A. Caicedo, Miguel Cervera, Michele Chiumenti & Joan Baiges

  2. Technical University of Catalonia, Edificio C1, Campus Norte, Gran Capitán s/n, 08034, Barcelona, Spain

    Miguel Cervera, Michele Chiumenti & Joan Baiges

  3. Technical University of Catalonia, Av. Eduard Maristany 16 (EEBE), 08019, Barcelona, Spain

    Manuel A. Caicedo

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  1. Carlos A. Moreira
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Moreira, C.A., Caicedo, M.A., Cervera, M. et al. An accurate, adaptive and scalable parallel finite element framework for the part-scale thermo-mechanical analysis in metal additive manufacturing processes. Comput Mech 73, 983–1011 (2024). https://doi.org/10.1007/s00466-023-02397-6

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