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Mesh sensitivity study in the random cellular automata finite element model of dynamic recrystallization
SITKO Mateusz, PAWLIKOWSKI Kacper, PERZYNSKI Konrad, MADEJ Lukasz
download PDFAbstract. Predicting microstructure morphology evolution under hot forming conditions and determining final material properties are essential for optimizing metal-forming processes. Cellular Automata (CA) is a widely employed full-field method for modeling microstructure morphology changes during various metal-forming processes. However, at higher temperatures and under conditions of substantial microstructure evolution, the CA method encounters limitations related to computational domain geometry changes. The use of random cellular automata (RCA) offers a more realistic representation of this phenomenon, although it requires additional effort in algorithm optimization for acceptable execution times.
This paper contributes to an overarching research effort focused on developing a discontinuous dynamic recrystallization model (DRX) by directly incorporating RCA into the finite element (FE) framework. Different mesh sizes and their impact on the quality of the results are analyzed, and the minimum number of elements that do not degrade the results in the CA model are selected. The investigation aims to enhance the practicality of the proposed model, striking a balance between realistic microstructure representation and computational efficiency.
Keywords
Random Cellular Automata, Microstructure Evolution, Discontinuous Dynamic Recrystallization
Published online 4/24/2024, 7 pages
Copyright © 2024 by the author(s)
Published under license by Materials Research Forum LLC., Millersville PA, USA
Citation: SITKO Mateusz, PAWLIKOWSKI Kacper, PERZYNSKI Konrad, MADEJ Lukasz, Mesh sensitivity study in the random cellular automata finite element model of dynamic recrystallization, Materials Research Proceedings, Vol. 41, pp 2271-2277, 2024
DOI: https://doi.org/10.21741/9781644903131-250
The article was published as article 250 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.
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