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Tytuł artykułu

Modelling the high‑temperature deformation characteristics of S355 steel using artificial neural networks

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Języki publikacji
EN
Abstrakty
EN
In this study, artificial neural networks were used to predict the plastic flow behaviour of S355 steel in the process of high-temperature deformation. The aim of the studies was to develop a model of changes in stress as a function of strain, strain rate and temperature, necessary to build an advanced numerical model of the soft-reduction process. The high-temperature characteristics of the tested steel were determined with a Gleeble 3800 thermo-mechanical simulator. Tests were carried out in the temperature range of 400-1450 °C for two strain rates, i.e. 0.05 and 1 s-1. The test results were next used to develop and verify a rheological model based on artificial neural networks (ANNs). The conducted studies show that the selected models offer high accuracy in predicting the high-temperature flow behaviour of S355 steel and can be successfully used in numerical modelling of the soft-reduction process.
Rocznik
Strony
art. no. e1, 2023
Opis fizyczny
Bibliogr. 13 poz., rys., tab., wykr.
Twórcy
  • Faculty of Metals Engineering and Industrial Computer Science, AGH University of Science and Technology, Al. Adama Mickiewicza 30, 30‑059 Krakow, Poland
  • Faculty of Metals Engineering and Industrial Computer Science, AGH University of Science and Technology, Al. Adama Mickiewicza 30, 30‑059 Krakow, Poland
autor
  • Faculty of Metals Engineering and Industrial Computer Science, AGH University of Science and Technology, Al. Adama Mickiewicza 30, 30‑059 Krakow, Poland
Bibliografia
  • 1. Hojny M. Modeling steel deformation in the semi-solid state: advanced structured materials. Switzerland: Springer; 2018.
  • 2. Zhang L, Shen H, Rong Y. Numerical simulation on solidification and thermal stress of continuous casting billet in mold based on meshless methods. Mat Sci Eng. 2007;466(1-2):71-8.
  • 3. Kalaki A, Ketabchi M. Predicting the rheological behaviour of AISI D2 semi-solid steel by plastic instability approach. Am J Mat Eng Tech. 2013;1(3):41-5.
  • 4. Hojny M, Głowacki M, Bała P, Bednarczyk W, Zalecki W. Multiscale model of heating-remelting-cooling in the Gleeble 3800 thermo-mechanical simulator system. Arch Metall Mater. 2019;64(1):401-12.
  • 5. Lin Y, Chen M, Zhang J. Prediction of 42CrMo steel flow stress at high temperature and strain rate. Mech Res Commun. 2008;35:142-50.
  • 6. Reddy NS, Leeb YH, Parka CH, Lee CS. Prediction of flow stress in Ti-6Al-4V alloy with an equiaxed microstructure by artificial neural networks. Mater Sci Eng A. 2008;492:276-82.
  • 7. Cabrera JM, Omar AA, Jonas JJ, Prado JM. Modeling the flow behaviour of a medium carbon microalloyed steel under hot working conditions. Metall Mater Trans A. 1997;28:2233.
  • 8. Saravanan L, Velmurugan K, Venkatachalapathy VSK. Hot deformation behaviour and ANN modeling of an aluminium hybrid nanocomposite. Mater Today Proc. 2021;47:6594-9.
  • 9. Sani SA, Ebrahimi GR, Vafaeenezhad H, Kiani-Rashid AR. Modeling of hot deformation behaviour and prediction of flow stress in a magnesium alloy using constitutive equation and artificial neural network (ANN) model. J Magn Alloys. 2018;6:134-44.
  • 10. Lin YC, Huang J, Li H-B, Chen D-D. Phase transformation and constitutive models of a hot compressed TC18 titanium alloy in the α+β regime. Vacuum. 2018;157:83-91. https://doi.org/10.1016/j.vacuum.2018.08.020.
  • 11. Yonghua D, Lishi M, Huarong Q, Runyue L, Ping L. Developed constitutive models, processing maps and microstructural evolution of Pb-Mg-10Al-0.5B alloy. Mater Charact. 2017;129:353-66.
  • 12. Lin YC, Liang YJ, Chen MS, et al. A comparative study on phenomenon and deep belief network models for hot deformation behavior of an Al-Zn-Mg-Cu alloy. Appl Phys A. 2017;123:68. https://doi.org/10.1007/s00339-016-0683-6).
  • 13. Tadeusiewicz R. Neural networks in mining sciences-general overview and some representative examples. Arch Min Sci. 2015;60(4):971-84.
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-03ed31e5-8081-4618-837d-a096c777d3b6
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