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Stochastic model describing evolution of microstructural parameters during hot rolling of steel plates and strips

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Języki publikacji
EN
Abstrakty
EN
Enhancing strength-ductility synergy of materials has been for decades an objective of research on structural metallic materials. It has been shown by many researchers that significant improvement of this synergy can be obtained by tailoring heterogeneous multiphase microstructures. Since large gradients of properties in these microstructures cause a decrease of the local fracture resistance, the objective of research is to obtain smoother gradients of properties by control of the manufacturing process. Advanced material models are needed to design such microstructures with smooth gradients. These models should supply information about distributions of various microstructural features, instead of their average values. Models based on stochastic internal variables meet this requirement. Our objective was to account for the random character of the recrystallization and to transfer this randomness into equations describing the evolution of dislocations and grain size during hot deformation and during interpass times. The idea of this stochastic model is described in the paper. Experiments composed of uniaxial compression tests were performed to supply data for the identification and verification of the model in the hot deformation and static recrystallization parts. Histograms of the grain size were measured after hot deformation and at different times after the end of deformation. Identification and validation of the model were performed. The validated model, which predicts evolution of heterogeneous multiphase microstructure, is the main output of our work. The model was implemented in the finite element program for hot rolling of plates and sheets and simulations of these processes were performed. The model’s capability to compare and evaluate various rolling strategies are demonstrated in the paper.
Rocznik
Strony
art. no. e139
Opis fizyczny
Bibliogr. 36 poz., rys., tab., wykr.
Twórcy
  • Faculty of Metals Engineering and Industrial Computer Science, AGH University of Science and Technology, al. Mickiewicza 30, 30‑059 Krakow, Poland
  • Faculty of Applied Mathematics, AGH University of Science and Technology, al. Mickiewicza 30, 30‑059 Krakow, Poland
  • Faculty of Metals Engineering and Industrial Computer Science, AGH University of Science and Technology, al. Mickiewicza 30, 30‑059 Krakow, Poland
autor
  • Faculty of Metals Engineering and Industrial Computer Science, AGH University of Science and Technology, al. Mickiewicza 30, 30‑059 Krakow, Poland
autor
  • Łukasiewicz Research Network, Institute for Ferrous Metallurgy, ul. K. Miarki 12, 44‑100 Gliwice, Poland
  • Faculty of Applied Mathematics, AGH University of Science and Technology, al. Mickiewicza 30, 30‑059 Krakow, Poland
  • Faculty of Applied Mathematics, AGH University of Science and Technology, al. Mickiewicza 30, 30‑059 Krakow, Poland
  • Faculty of Metals Engineering and Industrial Computer Science, AGH University of Science and Technology, al. Mickiewicza 30, 30‑059 Krakow, Poland
  • Łukasiewicz Research Network, Institute for Ferrous Metallurgy, ul. K. Miarki 12, 44‑100 Gliwice, Poland
  • Faculty of Applied Mathematics, AGH University of Science and Technology, al. Mickiewicza 30, 30‑059 Krakow, Poland
Bibliografia
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  • 2. Chang Y, Lin M, Hangen U, Richter S, Haase C, Bleck W. Revealing the relation between microstructural heterogeneities and local mechanical properties of complex-phase steel by correlative electron microscopy and nanoindentation characterization. Mater Des. 2021;203: 109620.
  • 3. Hassan SF, Al-Wadei H. Heterogeneous microstructure of low-carbon microalloyed steel and mechanical properties. J Mater Eng Perform. 2020;29(11):7045-51.
  • 4. Heibel S, Dettinger T, Nester W, Clausmeyer T, Tekkaya AE. Damage mechanisms and mechanical properties of high-strength multi-phase steels. Materials. 2018;11:761.
  • 5. Li S, Vajragupta N, Biswas A, Tang W, Wang H, Kostka A, Yang X, Hartmaier A. Effect of microstructure heterogeneity on the mechanical properties of friction stir welded reduced activation ferritic/martensitic steel. Scripta Mater. 2022;207: 114306.
  • 6. Vajragupta N, Wechsuwanmanee P, Lian J, Sharaf M, Munstermann S, Ma A, Hartmaier A, Bleck W. The modeling scheme to evaluate the influence of microstructure features on microcrack formation of DP-steel: the artificial microstructure model and its application to predict the strain hardening behavior. Comput Mater Sci. 2014;94:198-213.
  • 7. Szeliga D, Chang Y, Bleck W, Pietrzyk M. Evaluation of using distribution functions for mean field modelling of multiphase steels. Proc Manuf. 2019;27:72-7.
  • 8. Pietrzyk M, Madej Ł, Rauch Ł, Szeliga D. Computational materials engineering: achieving high accuracy and efficiency in metals processing simulations. Elsevier, Amsterdam: Butterworth-Heinemann; 2015.
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  • 10. Madej Ł, Rauch Ł, Perzyński K, Cybułka P. Digital material representation as an efficient tool for strain inhomogeneities analysis at the micro scale level. Arch Civ Mech Eng. 2011;11:661-79.
  • 11. Bleck W, Prahl U, Hirt G, Bambach M. Designing new forging steels by ICMPE. In: Advances in production technology, Lecture Notes in Production Engineering, ed., Brecher C., 2015, 85-98.
  • 12. Klimczak K, Oprocha P, Kusiak J, Szeliga D, Morkisz P, Przybyłowicz P, Czyżewska N, Pietrzyk M. Inverse problem in stochastic approach to modelling of microstructural parameters in metallic materials during processing, Mathematical Problems in Engineering, 2022, Article ID 9690742.
  • 13. Szeliga D, Czyżewska N, Klimczak K, Kusiak J, Kuziak R, Morkisz P, Oprocha P, Pidvysotsk’yy V, Pietrzyk M, Przybyłowicz P. Identification and validation of the stochastic model describing evolution of microstructural parameters during hot forming of metallic materials (accepted for publication in the International Journal of Material Forming). https://doi.org/10.1007/s12289-022-01701-8.
  • 14. Tashkinov M. Statistical methods for mechanical characterization of randomly reinforced media. Mech Adv Materials Modern Process. 2017;3:18. https://doi.org/10.1186/s40759-017-0032-2.
  • 15. Cameron BC, Tasan CC. Microstructural damage sensitivity prediction using spatial statistics. Sci Rep. 2019;9:2774. https://doi.org/10.1038/s41598-019-39315-x.
  • 16. Napoli G, Di Schino A. Statistical modelling of recrystallization and grain growth phenomena in stainless steels: effect of initial grain size distribution. Open Eng. 2018;8:373-6.
  • 17. Poloczek Ł, Kuziak R, Pidvysotsk’yy V, Szeliga D, Kusiak J, Pietrzyk M, Physical and numerical simulations to predict distribution of microstructural features during thermomechanical processing of steels, Materials, 2022, 15, 1660, https://doi.org/10.3390/ma15051660.
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  • 19. Estrin Y, Mecking H. A unified phenomenological description of work hardening and creep based on one-parameter models. Acta Metall. 1984;32:57-70.
  • 20. Sandstrom R, Lagneborg R. A model for hot working occurring by recrystallization. Acta Metall. 1975;23:387-98.
  • 21. Czyżewska N, Kusiak J, Morkisz P, Oprocha P, Pietrzyk M, Przybyłowicz P, Rauch Ł, Szeliga D. On mathematical aspects of evolution of dislocation density in metallic materials, Nonlinear Analysis: Real World Applications, 2022, https://arxiv.org/abs/2011.08504 (submitted).
  • 22. Szeliga D, Czyżewska N, Klimczak K, Kusiak J, Morkisz P, Oprocha P, Pietrzyk M, Przybyłowicz P. Sensitivity analysis, identification and validation of the dislocation density based model for metallic materials. Metallurgical Res Technol. 2021;118:317. https://doi.org/10.1051/metal/2021037.
  • 23. Sellars CM. Physical metallurgy of hot working. In: Hot working and forming processes, eds, Sellars C.M., Davies G.J., The Metals Society, London, 1979, p. 3-15.
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  • 25. Roucoules C, Pietrzyk M, Hodgson PD. Analysis of work hardening and recrystallization during the hot working of steel using a statistically based internal variable method. Mater Sci Eng A. 2003;A339:1-9.
  • 26. Gavrus A, Massoni E, Chenot JL. An inverse analysis using a finite element model for identification of rheological parameters. J Mater Process Technol. 1996;60:447-54.
  • 27. Szeliga D, Gawąd J, Pietrzyk M. Inverse analysis for identification of rheological and friction models in metal forming. Comput Methods Appl Mech Eng. 2006;195:6778-98.
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  • 30. Pidvysots’kyy V. Model termo-mechanicznego kucia odkuwek dla przemysłu motoryzacyjnego z uwzględnieniem stanu struktury, PhD Thesis, IMŻ Gliwice, 2016, (in Polish).
  • 31. Bzowski K, Kitowski J, Kuziak R, Uranga P, Gutierrez I, Jacolot R, Rauch Ł, Pietrzyk M. Development of the material database for the VirtRoll computer system dedicated to design of an optimal hot strip rolling technology, Computer Methods in Materials. Science. 2017;17:225-46.
  • 32. Kobayashi S, Oh SI, Altan T. Metal forming and the finite element method. New York, Oxford: Oxford University Press; 1989.
  • 33. Pietrzyk M. Finite element simulation of large plastic deformation. J Mater Process Technol. 2000;106:223-9.
  • 34. Svietlichnyj D., Pietrzyk M. On-line model for control of hot plate rolling, Proc. Conf. on Modelling of Metal Rolling Processes 3, eds, Beynon J.H., Clark M.T., Ingham P., Kern P., Waterson K., London, 1999, p. 62-71.
  • 35. Tanaka T. Controlled rolling of steel plate and strip. Int Metals Rev. 1981;26:185-212.
  • 36. Kitowski J, Rauch Ł, Pietrzyk M, Perlade A, Jacolot R, Diegelmann V, Neuer M, Gutierrez I, Uranga P, Isasti N, Larzabal G, Kuziak R, Diekmann U. Virtual strip rolling mill, European Commission Research Programme of the Research Fund for Coal and Steel, RFSR-CT-2013-00007, 2018.
Uwagi
PL
Opracowanie rekordu ze środków MEiN, umowa nr SONP/SP/546092/2022 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2022-2023)
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-1149a387-c8d6-4272-9be2-fb457891c4a5
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