The effect of model size and boundary conditions on the representativeness of digital material representation simulations of ferritic-pearlitic sample compression
Konrad Perzyński
AGH University of Science and Technology, Al. Mickiewicza 30, 30-059, Krakow, Poland.
DOI:
https://doi.org/10.7494/cmms.2022.2.0780
Abstract:
The main objective of this work is to investigate the representativeness of the digital material representation (DMR) models of ferritic-pearlitic steel generated by the hybrid cellular automata (CA) / Monte Carlo (MC) algorithm. Particular attention is focused on determining the effect of the size of the digital representation model on its representativeness under deformation conditions simulated with the finite element (FE) framework. In addition, the effect of periodic and non-periodic boundary conditions on the deformation behaviour of DMR models is analysed. A dedicated buffer zone approach applied the periodic boundary conditions on non-periodic finite element models. The results of equivalent stresses and strains and their average values are used to evaluate the differences between the models’ predictions.
Cite as:
Perzyński, K. (2022). The effect of model size and boundary conditions on the representativeness of digital material representation simulations of ferritic-pearlitic sample compression. Computer Methods in Materials Science, 22(2), 59-66. https://doi.org/10.7494/cmms.2022.2.0780
Article (PDF):
Keywords:
Representativeness, Digital material representation, Finite element method, Periodic boundary conditions
References:
Boguń, K., Sitko, M., Mojżeszko, M., & Madej, Ł. (2021). Cellular Automata-based computational library for development of digital material representation models of heterogenous microstructures. Archives of Civil and Mechanical Engineering, 21(2), 61. https://doi.org/10.1007/s43452-021-00211-9.
Johnson, G.R., & Cook, W.H. (1983). A constitutive model and data for metals subjected to large strains, high strain rates and high temperatures. In Proceedings of the 7th International Symposium on Ballistics, The Hague, 19–21 April 1983, 541–547.
Madej, L. (2017). Digital/virtual microstructures in application to metals engineering – A review. Archives of Civil and Mechanical Engineering, 17(4), 839–854. https://doi.org/10.1016/j.acme.2017.03.002.
Madej, Ł., Krużel, P., Cybułka, P., Perzyński, K., & Banaś, K. (2012). Generation of dedicated finite element meshes for multiscale applications with Delaunay triangulation and adaptive finite element – cellular automata algorithms. Computer Methods in Materials Science, 12(2), 85–96.
Madej, L., Wang, J., Perzynski, K., & Hodgson, P.D. (2014). Numerical modeling of dual phase microstructure behavior under deformation conditions on the basis of digital material representation. Computational Materials Science, 95, 651–662. https://doi.org/10.1016/j.commatsci.2014.08.035.
Madej, L., Sitko, M., Fular, A., Sarzyński, R., Wermiński, M., & Perzyński, K. (2021). Capturing local material heterogeneities in numerical modelling of microstructure evolution. Journal of Machine Engineering, 21(4), 29–48. https://doi.org/10.36897/jme/143086.
Radwański, K. (2016). Structural characterization of low-carbon multiphase steels merging advanced research methods with light optical microscopy. Archives of Civil and Mechanical Engineering, 16(3), 282–293. https://doi.org/10.1016/j.acme.2015.12.001.
Ranjan Yadav, R., Dewang, Y., Raghuwanshi, J., & Sharma, V. (2020). Finite element analysis of extrusion process using aluminum alloy. Materials Today: Proceedings, 24(2), 500–509. https://doi.org/10.1016/j.matpr.2020.04.302.
Szeliga, D., Chang, Y., Madej, L., Bzowski, K., Perzyński, K., Haase, C., Bleck, W. & Pietrzyk, M. (2022). Correlating the microstructural heterogeneity with local formability of cold‐rolled dual‐phase and complex‐phase steels through hardness gradients. Steel Research International, 93(9), 2200130. https://doi.org/10.1002/srin.202200130.
Szyndler, J., & Madej, Ł. (2015). Numerical analysis of the influence of number of grains, FE mesh density and friction coefficient on representativeness aspects of the polycrystalline digital material representation – Plane strain deformation case study. Computational Materials Science, 96(A), 200–213. https://doi.org/10.1016/j.commatsci.2014.08.037.
Szyndler, J., Perzyński, K., & Madej, Ł. (2016). Numerical analysis of data transfer quality in the 3D multi-scale uncoupled concurrent model connected with DMR. Computer Methods in Materials Science, 16(2), 97–103.
Torić, N., & Burgess, I.W. (2016). A unified rheological model for modelling steel behaviour in fire conditions. Journal of Constructional Steel Research, 127, 221–230. https://doi.org/10.1016/j.jcsr.2016.07.031.
Wang, S., Wang, X., Liu, X., & Li, C. (2021). Experiment and simulation of variable thickness rolling for 3D-profiled blank. Journal of Materials Processing Technology, 290, 116971. https://doi.org/10.1016/j.jmatprotec.2020.116971.
Zhang, Q., Felder, E., & Bruschi, S. (2009). Evaluation of friction condition in cold forging by using T-shape compression test. Journal of Materials Processing Technology, 209(17), 5720–5729. https://doi.org/10.1016/j.jmatprotec.2009.06.002.
Zhao, H. (1997). A constitutive model for metals over a large range of strain rates Identification for mild-steel and aluminium sheets. Materials Science and Engineering: A, 230(1–2), 95–99. https://doi.org/10.1016/S0921-5093(97)00024-5.