–
Influence of manufacturing defaults on the behavior of 3D printed lattice structures with a multiscale data-driven approach
COURT Clément, MARENIC Eduard, PASSIEUX Jean-Charles
download PDFAbstract. The inherent multiscale (MS) complexity of lattice materials necessitates specialized modeling techniques to predict their mechanical behavior accurately and efficiently. A combination of the multiscale finite element method (e.g. FE²), with a Data-Driven paradigm, leads to Multi Scale Data-Driven (MSDD) modeling procedure to simulate the behavior of these materials. On top of the inherent complexity of these materials, limitations of the Laser Powder Bed Fusion (LPBF) manufacturing process result in geometrical imperfections of the manufactured materials giving rise to so called as-manufactured configuration. Furthermore, theses imperfections have significant influence on the mechanical behavior and introduce biases and variance from expected behavior based on the modeling with initially designed lattice material, so called as-designed configuration. The proposed research is the first step in the reduction of the cost of MSDD approach by aligning the model’s accuracy with the manufacturing capabilities. The aim is to exploit the inherent variance in as-manufactured lattice material and to minimizing the material database to the essential material state points. To that end, in this work, using X-ray tomography, we create digital twins of various as-manufactured aluminum lattice unit cells and perform a number of simulations. The goal is to get further understanding of the variance in mechanical behavior resulting from geometrical variations and defects. These results will be further used to enhance the efficiency of the MSDD approach with no compromise on predictive capabilities.
Keywords
Lattice, Additive Manufacturing, Data-Driven, Multiscale, Digital Twins
Published online 4/24/2024, 8 pages
Copyright © 2024 by the author(s)
Published under license by Materials Research Forum LLC., Millersville PA, USA
Citation: COURT Clément, MARENIC Eduard, PASSIEUX Jean-Charles, Influence of manufacturing defaults on the behavior of 3D printed lattice structures with a multiscale data-driven approach, Materials Research Proceedings, Vol. 41, pp 2182-2189, 2024
DOI: https://doi.org/10.21741/9781644903131-240
The article was published as article 240 of the book Material Forming
Content from this work may be used under the terms of the Creative Commons Attribution 3.0 license. Any further distribution of this work must maintain attribution to the author(s) and the title of the work, journal citation and DOI.
References
[1] L. J. Gibson et M. F. Ashby, Cellular Solids: Structure and Properties. Cambridge University Press, 1997.
[2] B. Blakey-Milner et al., Metal additive manufacturing in aerospace: A review, Materials & Design, 209 (2021) 110008. https://doi.org/10.1016/j.matdes.2021.110008
[3] L. Riva, P. S. Ginestra, E. Ceretti, Mechanical characterization and properties of laser-based powder bed–fused lattice structures: a review, Int J Adv Manuf Technol, 113 (2021) 649 671, doi: 10.1007/s00170-021-06631-4
[4] L. Liu, P. Kamm, F. García-Moreno, J. Banhart, D. Pasini, Elastic and failure response of imperfect three-dimensional metallic lattices: the role of geometric defects induced by Selective Laser Melting, Journal of the Mechanics and Physics of Solids, 107 (2017) 160 184. https://doi.org/10.1016/j.jmps.2017.07.003
[5] F. Feyel, A multilevel finite element method (FE2) to describe the response of highly non-linear structures using generalized continua, Computer Methods in Applied Mechanics and Engineering, 192 (2003) 3233 3244. https://doi.org/10.1016/S0045-7825(03)00348-7
[6] B. A. Le, J. Yvonnet, et Q.-C. He, Computational homogenization of nonlinear elastic materials using neural networks, International Journal for Numerical Methods in Engineering, 104 (2015) 1061 1084,doi: 10.1002/nme.1586
[7] T. Kirchdoerfer et M. Ortiz, Data-driven computational mechanics, Computer Methods in Applied Mechanics and Engineering, 304 (2016) 81 101. https://doi.org/10.1016/j.cma.2016.02.001
[8] R. Xu et al., Data-driven multiscale finite element method: From concurrence to separation, Computer Methods in Applied Mechanics and Engineering, 363 (2020), 112893. https://doi.org/10.1016/j.cma.2020.112893
[9] A. Platzer, A. Leygue, L. Stainier, Stratégie adataptive de calcul multiéchelle piloté par les données, 15ème Colloque National en Calcul des Structures, mai 2022.
[10] R. Eggersmann, L. Stainier, M. Ortiz, et S. Reese, Model-free data-driven computational mechanics enhanced by tensor voting, Computer Methods in Applied Mechanics and Engineering, 373 (2021) 113499. https://doi.org/10.1016/j.cma.2020.113499
[11] H. Moulinec, P. Suquet, A numerical method for computing the overall response of nonlinear composites with complex microstructure, Computer Methods in Applied Mechanics and Engineering, 157 (1998) 69 94. https://doi.org/10.1016/S0045-7825(97)00218-1
[12] E. Sanchez-Palencia, Homogenization in mechanics: A survey of solved and open problems, Rend. Sem. Mat. Univers. Politecn. Torino, 1986.
[13] M. A. Bessa et al., A framework for data-driven analysis of materials under uncertainty: Countering the curse of dimensionality, Computer Methods in Applied Mechanics and Engineering, 320 (2017) 633 667. https://doi.org/10.1016/j.cma.2017.03.037