Evaluation of data transfer influence in coupled Monte Carlo finite element model on microstructure evolution predictions
WERMIŃSKI Mariusz, PERZYŃSKI Konrad, SITKO Mateusz, MADEJ Łukasz
download PDFAbstract. The processes of microstructure evolution under thermal loading occurring during welding or additive manufacturing are highly complex and challenging to observe experimentally. Therefore, in this work, to support experimental investigation, a hybrid numerical model based on the combination of the Monte Carlo (MC) and Finite Element (FE) method was developed to simulate both temperature profiles and corresponding grain growth phenomena at a micro scale explicitly. The welding simulation was selected as a case study. The discrete MC microstructure evolution model is based on the temperature distribution provided by the FE simulation with the Goldak heat source model. The temperature data are transferred from 3D FE space to cubic MC Pott’s domain in each time step. Direct coupling between the MC cells and FE cubic elements provides accurate results but significantly increases the simulation time. Therefore, coarser FE meshes are used in the research, but this requires applying reliable data interpolation techniques between the two methods. As a result, the temperature field stored in a coarse FE cloud of points is interpolated into regular high-resolution MC cell space in each time step. Evaluation of the influence of various interpolation methods, along with their parameters on the final microstructure morphology after welding, is presented within this work.
Keywords
Interpolation, Monte Carlo, Finite Element Method, Welding
Published online 4/19/2023, 8 pages
Copyright © 2023 by the author(s)
Published under license by Materials Research Forum LLC., Millersville PA, USA
Citation: WERMIŃSKI Mariusz, PERZYŃSKI Konrad, SITKO Mateusz, MADEJ Łukasz, Evaluation of data transfer influence in coupled Monte Carlo finite element model on microstructure evolution predictions, Materials Research Proceedings, Vol. 28, pp 1551-1558, 2023
DOI: https://doi.org/10.21741/9781644902479-167
The article was published as article 167 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] B. Wang, S.J. Hu, L. Sun, T. Freiheit, Intelligent welding system technologies: State-of-the-Art review and perspectives, J. Manuf. Syst. 56 (2020) 373-391. https://doi.org/10.1016/J.JMSY.2020.06.020
[2] A.V. Müller, D. Dorow-Gerspach, M. Balden, M. Binder, B. Buschmann, B. Curzadd, T. Loewenhoff, R. Neu, G. Schlick, J.H. You, Progress in additive manufacturing of pure tungsten for plasma-facing component applications, J. Nucl. Mater. 566 (2022) 153760. https://doi.org/10.1016/J.JNUCMAT.2022.153760
[3] S. Mercan, The effect of the amount of soldering wire on fatigue strength in joining steels and sockets, Int. J. Fatigue 127 (2019) 157–164. https://doi.org/10.1016/J.IJFATIGUE.2019.06.004
[4] J. Yanagimoto, D. Banabic, M. Banu, L. Madej, Simulation of metal forming – visualization of invisible phenomena in the digital era, CIRP Annals 71 (2022) 599-622. https://doi.org/10.1016/J.CIRP.2022.05.007
[5] F. Villaret, B. Hary, Y. de Carlan, T. Baudin, R. Logé, L. Maire, M. Bernacki, Probabilistic and deterministic full field approaches to simulate recrystallization in ODS steels, Comput. Mater. Sci. 179 (2020) 109646. https://doi.org/10.1016/J.COMMATSCI.2020.109646
[6] C.L. Park, P.W. Voorhees, K. Thornton, Application of the level-set method to the analysis of an evolving microstructure, Comput. Mater. Sci. 85 (2014) 46-58. https://doi.org/10.1016/J.COMMATSCI.2013.12.022
[7] D. Tourret, H. Liu, J.L. Lorca, Phase-field modeling of microstructure evolution: Recent applications, perspectives and challenges, Prog. Mater. Sci. 123 (2022) 100810. https://doi.org/10.1016/J.PMATSCI.2021.100810
[8] J. Goldak, A. Chakravarti, M. Bibby, A new finite element model for welding heat sources, Metall. Mater. Trans. B 15 (1984) 299-305. https://doi.org/10.1007/BF02667333
[9] A. Williamson, J.P. Delplanque, Investigation of Dynamic Abnormal Grain Growth Using the Monte Carlo Potts method, Comput. Mater. Sci. 124 (2016) 114-129. https://doi.org/10.1016/J.COMMATSCI.2016.07.025
[10] K. Boguń, M. Sitko, M. Mojżeszko, L. Madej, Cellular automata-based computational library for development of digital material representation models of heterogenous microstructures, Archiv. Civ. Mech. Eng. 21 (2021) 61. https://doi.org/10.1007/s43452-021-00211-9
[11] P.M. Bartier, C.P. Keller, Multivariate interpolation to incorporate thematic surface data using inverse distance weighting (IDW), Comput. Geosci. 22 (1996) 795-799. https://doi.org/10.1016/0098-3004(96)00021-0
[12] L.D.G. Sigalotti, O. Rendón, J. Klapp, C.A. Vargas, F. Cruz, A new insight into the consistency of the SPH interpolation formula, Appl. Math. Comput. 356 (2019) 50-73. https://doi.org/10.1016/j.amc.2019.03.018
