Methods of development fuzzy logic driven decision-support models in copper alloys processing

• Autorzy: Kluska-Nawarecka S. , Górny Z. , Mrzygłód B. , Wilk-Kołodziejczyk D. , Regulski K.
• Języki publikacji: EN

Abstrakty

EN

Development of a diagnostic decision support system using different then divalent logical formalism, in particular fuzzy logic, allows the inference from the facts presented not as explicit numbers, but described by linguistic variables such as the "high level", "low temperature", "too much content", etc. Thanks to this, process of inference resembles human manner in actual conditions of decision-making processes. Knowledge of experts allows him to discover the functions describing the relationship between the classification of a set of objects and their characteristics, on the basis of which it is possible to create a decision-making rules for classifying new objects of unknown classification so far. This process can be automated. Experimental studies conducted on copper alloys provide large amounts of data. Processing of these data can be greatly accelerated by the classification trees algorithms which provides classes that can be used in fuzzy inference model. Fuzzy logic also provides the flexibility of allocating to classes on the basis of membership functions (which is similar to events in real-world conditions). Decision-making in foundry operations often requires reliance on knowledge incomplete and ambiguous, hence that the conclusions from the data and facts may be "to some extent" true, and the technologist has to determine what level of confidence is acceptable, although the degree of accuracy for specific criteria is defined by membership function, which takes values from interval <0,1>. This paper describes the methodology and the process of developing fuzzy logic-based models of decision making based on preprocessed data with classification trees, where the needs of the diverse characteristics of copper alloys processing are the scope. Algorithms for automatic classification of the materials research work of copper alloys are clearly the nature of the innovative and promising hope for practical applications in this area.

Słowa kluczowe

EN

information technology; foundry industry; fuzzy logic; copper alloy; classification algorithm; classification trees; regression trees; decision making rules

PL

technologia informacyjna; przemysł odlewniczy; logika rozmyta; stop miedzi; algorytm klasyfikacji; drzewo klasyfikacyjne; drzewo regresji

• Wydawca: Komisja Odlewnictwa Polskiej Akademii Nauk Oddział w Katowicach
• Czasopismo: Archives of Foundry Engineering
• Rocznik: 2010
• Tom: Vol. 10, iss. 1 spec.
• Strony: 23--28
• Opis fizyczny: Bibliogr. 9 poz., rys., tab., wykr.

Twórcy

autor

Kluska-Nawarecka S.

• Polish Foundry Research Institute, Zakopiańska 73, Krakow, Polska

autor

Górny Z.

• Polish Foundry Research Institute, Zakopiańska 73, Krakow, Polska

autor

Mrzygłód B.

• Department of Industrial Computer Science, Faculty of Metals Engineering and Industrial Computer Science, AGH, Mickiewicza 30, Kraków, Polska

autor

Wilk-Kołodziejczyk D.

• The Andrzej Frycz Modrzewski Krakow University College, Kraków, Polska

autor

Regulski K.

• Department of Industrial Computer Science, Faculty of Metals Engineering and Industrial Computer Science, AGH, Mickiewicza 30, Kraków, Polska

Bibliografia

• [1] Zimmermann H.J., Zadeh L.A., Gaines A.R., Fuzzy Sets and Decision Analysis. North Holland, New York, 1984.
• [2] International Electrotechnical Commission (IEC), Technical Committee No. 65: Industrial Process Measurement And Control, IEC 1131 - Programmable Controllers, Part 7 - Fuzzy Control Programming, Committee Draft Cd1.0 (Rel. 19 Jan 97).
• [3] Drinkov, H. Hellendoorn & M. Reinfrank. An Introduction to Fuzzy Control. Springer-Verlag, 1996.
• [4] Piegat A., Fuzzy control and modeling (in Polish), EXIT, Warsaw 2003.
• [5] Mrzygłód B., Kluska-Nawarecka S., Adrian A., Durak J., Knowledge representation and reasoning in fuzzy logic as applied to the process of hot-dip galvanising, FOCOMP'2006 : simulation designing and control of foundry processes : the fifth international conference : Kraków, Poland, 22–24 November 2006.
• [6] Breinman, L., Friedman, J.H., Olshen, R.A. and Stone, C.J., Classification and regression trees. 1984. Wadsworth and Brooks, California.
• [7] J.R. Quinlan. “Induction on Decision Trees”. Machine Learning.
• [8] Hill, T. & Lewicki, P. (2007). STATISTICS Methods and Applications. StatSoft, Tulsa, OK.
• [9] Durak J., Wojtaszek M., Mrzygłód B., Application of Matlab's Fuzzy Toolbox in didactics of material science, Computer supports for didactics : Łódź, 25–27 June 2008 / ed. Edward Kącki, Joanna Stempczyńska ; WSI, 2008. — ISBN 978-83-60282-07-6.