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Approximation of Ausferrite Content in the Compacted Graphite Iron with the Use of Combined Techniques of Data Mining

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Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
This article presents the methodology for exploratory analysis of data from microstructural studies of compacted graphite iron to gain knowledge about the factors favouring the formation of ausferrite. The studies led to the development of rules to evaluate the content of ausferrite based on the chemical composition. Data mining methods have been used to generate regression models such as boosted trees, random forest, and piecewise regression models. The development of a stepwise regression modelling process on the iteratively limited sets enabled, on the one hand, the improvement of forecasting precision and, on the other, acquisition of deeper knowledge about the ausferrite formation. Repeated examination of the significance of the effect of various factors in different regression models has allowed identification of the most important variables influencing the ausferrite content in different ranges of the parameters variability.
Rocznik
Strony
117--122
Opis fizyczny
Bibliogr. 33 poz., rys., tab., wykr.
Twórcy
autor
  • AGH University of Science and Technology, Faculty of Metals Engineering and Industrial Computer Science, Mickiewicza 30, Kraków, Poland
  • AGH University of Science and Technology, Faculty of Metals Engineering and Industrial Computer Science, Mickiewicza 30, Kraków, Poland
  • Foundry Research Institute, ul. Zakopiańska 73, 30-418 Kraków, Poland
autor
  • Department of Materials Engineering and Production Systems, Lodz University of Technology, Stefanowskiego 1/15 Street, 90-924 Łódź, Poland
autor
  • Department of Materials Engineering and Production Systems, Lodz University of Technology, Stefanowskiego 1/15 Street, 90-924 Łódź, Poland
autor
  • AGH University of Science and Technology, Faculty of Metals Engineering and Industrial Computer Science, Mickiewicza 30, Kraków, Poland
  • AGH University of Science and Technology, Faculty of Metals Engineering and Industrial Computer Science, Mickiewicza 30, Kraków, Poland
Bibliografia
  • [1] Skvarenina, S. & Shin, Y.C. (2006). Laser-assisted machining of compacted graphite iron. International Journal of Machine Tools and Manufacture. 46(1), 7-17.
  • [2] Pietrowski, S. (2000). Compendium of knowledge about compacted cast iron. Solidification of Metals and Alloys. 2(44), 279-292. (in Polish).
  • [3] Guzik, E. & Kleingartner, T. (2009). A study on the structure and mechanical properties of compacted cast iron with pearlitic-ferritic matrix. Archives of Foundry Engineering. 9(3), 55-60.
  • [4] Mierzwa, P. & Soiński, M.S. (2011). The effect of thermal treatment on the mechanical properties of compacted cast iron. Archives of Foundry Engineering. 10(spec.1), 133-138.
  • [5] Pytel, A. & Gazda, A. (2014). Evaluation of selected properties in austempered compacted cast iron (AVCI). Transactions of Foundry Research Institute. LIV(4), 23-31. DOI: 10.7356/iod.2014.18.
  • [6] Soiński, M.S. & Jakubus, A. (2014). Initial Assessment of Abrasive Wear Resistance of Austempered Cast Iron with Compacted Graphite. Archives of Metallurgy and Materials. 59(3), 1073-1076. DOI: 10.2478/amm-2014-0183.
  • [7] Regulski, K., Wilk-Kołodziejczyk, D. & Gumienny, G. (2016). Comparative analysis of the properties of the Nodular Cast Iron with Carbides and the Austempered Ductile Iron with use of the machine learning and the Support Vector Machine. The International Journal of Advanced Manufacturing Technology. 87(1), 1077-1093. DOI: 10.1007/s00170-016-8510-y.
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  • [19] Shaikhina, T., Lowe, D., Daga, S., Briggs, D., Higgins, R. & Khovanova (2017). Decision tree and random forest models for outcome prediction in antibody incompatible kidney transplantation. Biomed. Signal Process Control. http://dx.doi.org/ 10.1016/j.bspc.2017.01.012.
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  • [30] David, J., Jancikova, Z., Frischer, R. & Vrozina, M. (2013). Crystallizer's Desks Surface Diagnostics with Usage of Robotic System. Archives of Metallurgy and Materials. 58(3), 907-910.
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Uwagi
PL
Opracowanie ze środków MNiSW w ramach umowy 812/P-DUN/2016 na działalność upowszechniającą naukę (zadania 2017)
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-712114e2-938c-4d5b-9c81-146e673d0209
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