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Rule modeling of ADI cast iron structure for contradictory data

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Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
Ductile iron is a material that is very sensitive to the conditions of crystallization. Due to this fact, the data on the cast iron properties obtained in tests are significantly different and thus sets containing data from samples are contradictory, i.e. they contain inconsistent observations in which, for the same set of input data, the output values are significantly different. The aim of this work is to try to determine the possibility of building rule models in conditions of significant data uncertainty. The paper attempts to determine the impact of the presence of contradictory data in a data set on the results of process modeling with the use of rule-based methods. The study used the well-known dataset (Materials Algorithms Project Data Library, n.d.) pertaining to retained austenite volume fraction in austempered ductile cast iron. Two methods of rulebased modeling were used to model the volume of the retained austenite: the decision trees algorithm (DT) and the rough sets algorithm (RST). The paper demonstrates that the number of inconsistent observations depends on the adopted data discretization criteria. The influence of contradictory data on the generation of rules in both algorithms is considered, and the problems that can be generated by contradictory data used in rule modeling are indicated.
Wydawca
Rocznik
Strony
211--228
Opis fizyczny
Bibliogr. 23 poz., rys.
Twórcy
  • Warsaw University of Technology, Institute of Manufacturing Technologies, Warsaw, Poland
  • Warsaw University of Technology, Institute of Manufacturing Technologies, Warsaw, Poland
  • Warsaw University of Technology, Institute of Manufacturing Technologies, Warsaw, Poland
Bibliografia
  • Barbosa, P.A., Costa, É.S., & Guesser, W.L., & Machado, Á.R. (2015). Comparative study of the machinability of austempered and pearlitic ductile irons in drilling process. Journal of the Brazilian Society of Mechanical Sciences and Engineering, 37(1), 115–122. https://doi.org/10.1007/s40430-014-0161-z.
  • Chawla, V., Batra, U., Puri, D., & Chawla, A. (2008). To study the effect of austempering temperature on fracture behaviour of Ni-Mo Austempered Ductile Iron (ADI). Journal of Minerals & Materials Characterization & Engineering, 7(4), 307–316. https://doi.org/10.4236/jmmce.2008.74024.
  • Colin García, E.; Cruz Ramírez, A., Reyes Castellanos, G., Chávez Alcalá, J.F., Téllez Ramírez, J., & Magaña Hernández, A. (2021). Heat treatment evaluation for the camshafts production of ADI low alloyed with Vanadium. Metals, 11(7), 1036. https://doi.org/10.3390/met11071036.
  • Dal Corobbo, M., Arias, S. (2009). Evaluation of impact properties of austempered ductile iron. [Master’s thesis, Department of Materials and Manufacturing Technology, Chalmers University of Technology]. Printed by Chalmers Reproservice Göteborg, Sweden.
  • Grzegorzewski, P., & Kochański, A. (2019). Data preprocessing in industrial manufacturing. In P. Grzegorzewski, A. Kochański, J. Kacprzyk (Eds.), Soft Modeling in Industrial Manufacturing (pp. 75–88). Springer Cham. https://doi.org/10.1007/978-3-030-03201-2_3.
  • Heydarzadeh Sohi, M., Nili Ahmadabadi, M., & Bahrami Vahdat, A. (2004). The role of austempering parameters on the structure and mechanical properties on heavy section ADI. Journal of Materials Processing Technology, 153–154, 203–208. https://doi.org/10.1016/J.JMATPROTEC.2004.04.308.
  • Kochański, A., Perzyk, M., & Kłębczyk, M. (2012). Knowledge in imperfect data. In C. Ramirez (Ed.), Advances in Knowledge Representation (pp. 181–210). InTech. https://doi.org/10.5772/37714.
  • Kochański, A., Soroczyński, A., & Kozłowski, J. (2013). Applying rough set theory for the modeling of austempered ductile iron properties. Archives of Foundry Engineering, 13(Special Issue 2), 70–73.
  • Kochański, A., Grzegorzewski, P., Soroczyński, A., & Olwert, A. (2014). Modeling of austempered ductile iron using discrete signals. Computer Methods in Materials Science, 14(3), 190–196.
  • Kochański, A., Krzyńska, A., Chmielewski, T., & Stoliński, A. (2015). Comparison of austempered ductile iron and manganese steel wearability. Archives of Foundry Engineering, 15(Special Issue 1), 51–54.
  • Materials Algorithms Project Data Library (n.d.). Data Library MAP_DATA_ADI_RETAINED-AUSTENITE. http://www.phase-trans.msm.cam.ac.uk/map/data/materials/adiret.html.
  • Nobuki, T., Hatate, M., & Shiota, T. (2010). Notch effects on impact and bending characteristics of spheroidal graphite and compacted vermicular graphite cast irons with various matrices. Key Engineering Materials, 457, 392–397. https://doi.org/10.4028/www.scientific.net/KEM.457.392.
  • Olofsson, J., Larsson, D., & Svensson, I.L. (2011). Effect of austempering on plastic behavior on some austempered ductile iron alloys. Metallurgical and Materials Transactions A, 42(13), 3999–4007. https://doi.org/10.1007/s11661-011-0796-7.
  • Perzyk, M., & Kochański, A.W. (2001). Prediction of ductile cast iron quality by artificial neural networks. Journal of Materials Processing Technology, 109(3), 305–307. https://doi.org/10.1016/S0924-0136(00)00822-0.
  • Perzyk, M., & Soroczynski, A. (2008). Comparison of selected tools for generation of knowledge for foundry production. Archives of Foundry Engineering, 8(4), 163–166.
  • Perzyk, M., & Soroczyński, A. (2019). Assessment of selected tools used for knowledge extraction in industrial manufacturing. In P. Grzegorzewski, A. Kochański, J. Kacprzyk, J. (Eds.), Soft Modeling in Industrial Manufacturing (pp. 27–41). Springer Cham. https://doi.org/10.1007/978-3-030-03201-2_5.
  • Perzyk, M., Biernacki, R., Kochanski, A., Kozlowski, J., & Soroczynski, A. (2011). Applications of data mining to diagnosis and control of manufacturing processes. In K. Funatsu (Eds.). Knowledge-oriented applications in data mining (pp. 147–166). InTech. https://doi.org/10.5772/13282.
  • Perzyk, M., Kochański, A., Kozłowski, J., & Myszka, D. (2015). Control of ductile iron austempering process by advanced data driven modeling. In 71st World Foundry Congress: Advanced Sustainable Foundry (WFC 2014). 19–21 May 2014, Bilbao, Spain. https://www.scopus.com/record/display.uri?eid=2-s2.0-84928901998&origin=resultslist&sort=plf-f.
  • Rodríguez-Rosales, N.A., Montes-González, F.A., Gómez-Casas, O., Gómez-Casas, J., Galindo-Valdés, J.S., Ortiz-Cuellar, J.C., Martínez-Villafañe, J.F., García-Navarro, D., & Muñiz-Valdez, C.R. (2022). Statistical data-driven model for hardness prediction in austempered ductile irons. Metals, 12(4), 676. https://doi.org/10.3390/met12040676.
  • Stefanowski, J., & Vanderpooten, D. (2001). Induction of decision rules in classification and discovery‐oriented perspectives. International Journal of Intelligent Systems, 16(1), 13–27. https://doi.org/10.3390/ma15082709.
  • Wilk-Kołodziejczyk, D., Regulski, K., Giętka, T., Gumienny, G., Jaśkowiec, K., & Kluska-Nawarecka, S. (2018). The selection of heat treatment parameters to obtain austempered ductile iron with the required impact strength. Journal of Materials Engineering and Performance, 27(11), 5865–5878. https://doi.org/10.1007/s11665-018-3714-y.
  • Wohlfahrt, M., Oberwinkler, C., Tunzini, S., Rauscher, A., Prida Caballero, R., de la, & Eichlseder, W. (2010). The role of sampling position on fatique of austempered ductile iron. Procedia Engineering, 2(1), 1337–1341. https://doi.org/10.1016/j.proeng.2010.03.145.
  • Yescas, M.A., Bhadeshia, H.K.D.H., & MacKay, D.J. (2001). Estimation of the amount of retained austenite in austempered ductile irons using neural networks. Materials Science and Engineering: A, 311(1–2), 162–173. https://doi.org/10.1016/S0921-5093(01)00913-3.
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-e9f33968-004d-4df6-8e9d-5c479bbf85a3
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