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Bainite transformation time model optimization for Austempered Ductile Iron with the use of heuristic algorithms

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Języki publikacji
EN
Abstrakty
EN
The paper presents the application of heuristic optimization methods in identifying the parameters of a model for bainite transformation time in ADI (Austempered Ductile Iron). Two algorithms were selected for parameter optimization – Particle Swarm Optimization and Evolutionary Optimization Algorithm. The assumption of the optimization process was to obtain the smallest normalized mean square error (objective function) between the time calculated on the basis of the identified parameters and the time derived from the experiment. As part of the research, an analysis was also made in terms of the effectiveness of selected methods, and the best optimization strategies for the problem to be solved were selected on their basis.
Wydawca
Rocznik
Strony
125--136
Opis fizyczny
Bibliogr. 17 poz., rys.
Twórcy
  • AGH University of Science and Technology, al. Mickiewicza 30, 30-059 Krakow, Poland
  • AGH University of Science and Technology, al. Mickiewicza 30, 30-059 Krakow, Poland
  • AGH University of Science and Technology, al. Mickiewicza 30, 30-059 Krakow, Poland
  • AGH University of Science and Technology, al. Mickiewicza 30, 30-059 Krakow, Poland
Bibliografia
  • Boccardo, A.D., Dardati, P.M., Celentano, D.J., Godoy, L.A., Górny, M., & Tyrała E. (2015). Numerical Simulation of Austempering Heat Treatment of Ductile Cast Iron, Metallurgical and Materials Transactions B, 47(1), 566–575. https://doi.org/10.1007/s11663-015-0511-y.
  • Chester, N.A., & Bhadeshia, H.K.D.H. (1997). Mathematical modelling of bainite transformation kinetics. Journal de Physique, 07(C5), C5-41–C5-46. https://doi.org/10.1051/jp4:1997506.
  • Dai, H.-P., Chen, D.-D., & Zheng, Z.-S. (2018). Effects of random values for Particle Swarm Optimization Algorithm. Algorithms, 11(2), 23. https://doi.org/10.3390/a11020023.
  • Eberhart, R.C., & Shi, Y. (2001). Particle swarm optimization: developments, applications and resources. In Proceedings of the 2001 Congress on Evolutionary Computation (IEEE Cat. No.01TH8546) (pp. 81–86, vol. 1). https://doi.org/10.1109/CEC.2001.934374.
  • Essaid, M., Idoumghar, L., Lepagnot, J., Brévilliers, M., & Fodorean, D. (2018). A hybrid optimization algorithm for electric motor design. In Y. Shi, H. Fu, Y. Tian, V.V. Krzhizhanovskaya, M.H. Lees, J. Dongarra, P.M.A. Sloot (eds), Computational Science – ICCS 2018. 18th International Conference, Wuxi, China, June 11–13, 2018, Proceedings (part II), Springer Cham. https://doi.org/10.1007/978-3-319-93701-4_39.
  • Gili, M., Maringer, D., & Schumann, E. (2019). Numerical Methods and Optimization in Finance (2nd ed.). Elsevier–Academic Press.
  • Hepp, E., Hurevich, V., & Schäfer, W. (2012). Integrated modeling and hest treatment simulation of austempered ductile iron. IOP Conference Series: Materials Science and Engineering, 33, 1–10. https://doi.org/10.1088/1757-899X/33/1/012076.
  • Kuziak, R., Zalecki, W., & Pietrzyk, M. (2010). Matematyczne modelowanie hartowności bainitycznej. Prace Instytutu Metalurgii Żelaza, 62(1). 27–32.
  • McCaffrey, J. (2012). Test run – evolutionary optimization algorithms. MSDN Magazine, 27(6).
  • Mrzygłód, B., Kowalski, A., Olejarczyk-Wożeńska, I., Giętka, T., & Głowacki, M. (2017). Characteristics of ADI ductile cast iron with single addition of 1.56% Ni. Archives of Metallurgy and Materials, 62(4), 2273–2280. https://doi.org/10.1515/amm-2017-0335.
  • Nofal, A. (2013). Advances in the metallurgy and applications of ADI. Journal of Metallurgical Engineering, 2(1), 1–18.
  • Olejarczyk-Wożeńska, I., Adrian, H., Mrzygłód, B., & Głowacki, M. (2017). Mathematical model of bainitic transformation in austempered ductile iron. Archives of Foundry Engineering, 17(4), 200–206. https://doi.org/10.1515/afe-2017-0158.
  • Rees, G.I., & Bhadeshia H.K.D.H. (1992). Bainite transformation kinetics. Part 1. Modified model. Materials Science and Technology, 8(11), 985–993. https://doi.org/10.1179/mst.1992.8.11.985.
  • Sumathi, S., Hamsapriya, T., & Surekha, P. (2008). Evolutionary Intelligence. An Introduction to Theory and Applications with Matlab. Springer-Verlag Berlin Heidelberg.
  • Tang, L., Zhao, Y., & Liu, J. (2013). An improved differential evolution algorithm for practical dynamic scheduling in steelmaking-continuous casting production. IEEE Transactions on Evolutionary Computation, 18(2), 209–225. https://doi.org/10.1109/TEVC.2013.2250977.
  • Wang, Z., Sun, X., & Zhang, D. (2007). A PSO-based classification rule mining algorithm. In Huang, D.S., Heutte, L., & Loog, M. (eds), Advanced Intelligent Computing Theories and Applications. With Aspects of Theoretical and Methodological Issues. Third International Conference of Intelligent Computing, ICIC 2007, Qingdao, China, August 21–24, 2007 Proceedings (pp. 377–384). Springer Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-74205-0_42.
  • Zimba, J., Henwood, D., Navara, E., & Simbi, D.J. (1999). Three-dimensional diffusion model for austenitization of ferritic spheroidal graphite irons. Materials Science and Technology, 15(9), 1024–1030. https://doi.org/10.1179/026708399101506887.
Uwagi
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-ddaa957f-bc6a-45a5-87ad-c7924af90beb
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