Rule modeling of ADI cast iron structure for contradictory data

• Autorzy: Soroczyński Artur , Biernacki Robert , Kochański Andrzej

Identyfikatory

DOI

10.7494/cmms.2022.4.0791

• 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.

Słowa kluczowe

EN

rule modeling; contradictory data set; uncertainty; data preparation; decision tree; rough set theory

PL

niepewność; drzewo decyzyjne; teoria zbiorów przybliżonych

• Wydawca: Wydawnictwa AGH
• Czasopismo: Computer Methods in Materials Science
• Rocznik: 2022
• Tom: Vol. 22, No. 4
• Strony: 211--228
• Opis fizyczny: Bibliogr. 23 poz., rys.

Twórcy

autor

Soroczyński Artur

• Warsaw University of Technology, Institute of Manufacturing Technologies, Warsaw, Poland

• https://orcid.org/0000-0002-5935-3690

autor

Biernacki Robert

• Warsaw University of Technology, Institute of Manufacturing Technologies, Warsaw, Poland

• https://orcid.org/0000-0002-2294-5879

autor

Kochański Andrzej

• Warsaw University of Technology, Institute of Manufacturing Technologies, Warsaw, Poland

• https://orcid.org/0000-0002-1060-1390

Bibliografia

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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).