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Research Article | Open Access

Efficient thermomechanical modelling of Laser Powder Bed Fusion additive manufacturing process with emphasis on parts residual stress fields

Harry O. Psihoyos1( )George N. Lampeas2
ATHENA Research Center, Industrial Systems Institute, Patras Science Park Building Platani, Patras 26504, Greece
Laboratory of Technology and Strength of Materials, Department of Mechanical Engineering & Aeronautics, University of Patras, Rion 26504, Western Greece, Greece
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Abstract

Laser Powder Bed Fusion (LPBF) process is one of the advanced Additive Manufacturing (AM) processes, which is employed for the fabrication of complex metallic components. One of the major drawbacks of the LPBF is the development of residual stresses due to the high temperature gradients developed during the process thermal cycles. Reliable models for the prediction of residual strain and stress at part scale are required to support the LPBF process optimization. Due to the computational cost of the LPBF simulation, the current modelling methodology utilizes assumptions to make feasible the prediction of residual stresses at parts or component level. To this scope, a thermomechanical modelling approach for the simulation of LPBF process is presented with focus to residual stress and strain prediction. The modelling efficiency of the proposed approach was tested on a series on cases for which experimental data were available. The good comparison between the predicted and experimental data validated the modelling method. The efficiency of the thermomechanical modelling method is demonstrated by the reduced computational time required.

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AIMS Materials Science
Pages 455-480

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Cite this article:
Psihoyos HO, Lampeas GN. Efficient thermomechanical modelling of Laser Powder Bed Fusion additive manufacturing process with emphasis on parts residual stress fields. AIMS Materials Science, 2022, 9(3): 455-480. https://doi.org/10.3934/matersci.2022027

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Received: 29 December 2021
Revised: 05 April 2022
Accepted: 14 April 2022
Published: 15 June 2022
©2022 the Author(s), licensee AIMS Press.

This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0)