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On the Modeling and Simulation of SLM and SLS for Metal and Polymer Powders: A Review

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Abstract

Additive Manufacturing concentrates the attention, not only of the research and academic community, but of the industry as well. Selective Laser Melting (SLM) and Selective Laser Sintering (SLS) are among the broadest employed methods in AM, since they can treat almost all types of materials. Along with the extensive experimental research that is carried out regarding SLS and SLM, modeling and simulation are powerful tools allowing better and more in depth understanding of the processes. Nevertheless, there is no general framework in modeling, but mainly studies and proposed modeling approaches. The current paper reviews modeling methods and techniques that in literature are presented for the simulation of SLM and SLS. Besides the Finite Element Method, which is the most common method used, other numerical methods like Discrete Element Method, Smoothed Particles Hydrodynamics and Molecular Dynamics have been overviewed as well. The heat transfer and fluid dynamics models consist the main core of every simulation, while other sub-models are integrated to estimate parameters like residual stresses, part deformation, material microstructure, or crystallization. The main scope of the current paper is to provide a comprehensive and detailed review on the modeling and simulation of SLS/SLM and to inform the reader concerning the different modeling strategies.

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  1. School of Mechanical Engineering, Department of Manufacturing Technology, National Technical University of Athens, Athens, Greece

    Emmanouil L. Papazoglou, Nikolaos E. Karkalos, Panagiotis Karmiris-Obratański & Angelos P. Markopoulos

  2. Faculty of Mechanical Engineering and Robotics - Department of Manufacturing Systems, AGH University of Science and Technology, Cracow, Poland

    Panagiotis Karmiris-Obratański

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Papazoglou, E.L., Karkalos, N.E., Karmiris-Obratański, P. et al. On the Modeling and Simulation of SLM and SLS for Metal and Polymer Powders: A Review. Arch Computat Methods Eng 29, 941–973 (2022). https://doi.org/10.1007/s11831-021-09601-x

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