A Method to Make Classification of the Heat Treatment Processes Performed on Bronze Using Incomplete Knowledge

• Autorzy: Kluska-Nawarecka S. , Górny Z. , Regulski K. , Wilk-Kołodziejczyk D. , Jančíková Z. , David J.

Identyfikatory

DOI

10.2478/afe-2014-0039

• Języki publikacji: EN

Abstrakty

EN

The article describes the problem of selection of heat treatment parameters to obtain the required mechanical properties in heat- treated bronzes. A methodology for the construction of a classification model based on rough set theory is presented. A model of this type allows the construction of inference rules also in the case when our knowledge of the existing phenomena is incomplete, and this is situation commonly encountered when new materials enter the market. In the case of new test materials, such as the grade of bronze described in this article, we still lack full knowledge and the choice of heat treatment parameters is based on a fragmentary knowledge resulting from experimental studies. The measurement results can be useful in building of a model, this model, however, cannot be deterministic, but can only approximate the stochastic nature of phenomena. The use of rough set theory allows for efficient inference also in areas that are not yet fully explored.

Słowa kluczowe

EN

information technology; foundry industry; heat treatment; classification algorithms; rough sets; data mining

PL

technologia informacyjna; przemysł odlewniczy; obróbka cieplna; algorytmy klasyfikacyjne; zbiory przybliżone

• Wydawca: Komisja Odlewnictwa Polskiej Akademii Nauk Oddział w Katowicach
• Czasopismo: Archives of Foundry Engineering
• Rocznik: 2014
• Tom: Vol. 14, iss. 2
• Strony: 69--72
• Opis fizyczny: Bibliogr. 12 poz., rys., tab., wykr.

Twórcy

autor

Kluska-Nawarecka S.

• Foundry Research Institute, Cracow, Poland

autor

Górny Z.

• Foundry Research Institute, Cracow, Poland

autor

Regulski K.

• AGH University of Science and Technology, Cracow, Poland

autor

Wilk-Kołodziejczyk D.

• Foundry Research Institute, Cracow, Poland
• AGH University of Science and Technology, Cracow, Poland

autor

Jančíková Z.

• VŠB – Technical University of Ostrava, Ostrava-Poruba, Czech Republic

autor

David J.

• VŠB – Technical University of Ostrava, Ostrava-Poruba, Czech Republic

Bibliografia

• [1] Kluska-Nawarecka, S. Górny, Z. Mrzygłód, B. Wilk-Kołodziejczyk D. & Regulski, K. (2010). Methods of development fuzzy logic driven decision-support models in copper alloys processing, Archives of Foundry Engineering. vol. 10 spec. iss. 1. 23–28.
• [2] Wilk-Kołodziejczyk, D. Regulski, K. Dziaduś-Rudnicka, J. & Kluska-Nawarecka, S. (2012). Overview of activities on the Internet devoted to casting technology, Archives of Foundry Engineering. vol. 12 iss. 2, 245–250.
• [3] Gorny, Z. Kluska-Nawarecka, S. Wilk-Kolodziejczyk, D. & Regulski, K. (2010). Diagnosis of casting defects using uncertain and incomplete knowledge, Archives of Metallurgy and Materials. Volume 55 Issue 3. 827-836.
• [4] Nawarecki, E. Kluska-Nawarecka, S. Regulski, K. (2012). Multi-aspect character of the man-computer relationship in a diagnostic-advisory system, Human – computer systems interaction: backgrounds and applications 2, eds. Zdzisław S. Hippe, Juliusz L. Kulikowski, Teresa Mroczek. — Berlin ; Heidelberg : Springer-Verlag.
• [5] Mrzygłód, B. & Regulski, K. (2011). Model of knowledge representation about materials in the form of a relational database for CAPCAST system, Archives of Foundry Engineering. vol. 11 iss. 3, 81–86.
• [6] Spicka, I. & Heger, M. (2013). Utilization Mathematical and Physical Models Derived Therefrom Real-Time Models for the Optimization of Heating Processes; Archives of Metallurgy and Materials. Volume 58. Issue 3. 981-985.
• [7] Kluska-Nawarecka, S. Wilk-Kolodziejczyk, D. Smolarek-Grzyb, A. & Adrian, A. (2007). Knowledge Representation of Casting Metal Defects by Means of Ontology, Archives of Foundry Engineering. vol. 7 iss. 3, 75–78.
• [8] David, J. Svec, P. Frischer, R. & Garzinova, R. (2014).The Computer Support of Diagnostics of Circle Crystallizers; Metalurgija, 53 (2) 193-196; APR-JUN.
• [9] David, J. Jancikova, Z. Frischer, R. & Vrozina, M. (2013). Crystallizer's Desks Surface Diagnostics with Usage of Robotic System; Archives of Metallurgy and Materials Volume 58. Issue 3. 907-910.
• [10] Grzymala-Busse J., & Grzymala-Busse, W. (2007). An experimental comparison of three rough set approaches to missing attribute values, Transactions on Rough Sets 6: 31–50.
• [11] Bazan, J.G. Szczuka, M.S. Wróblewski, J. (2002). A new version of rough set exploration system. In: James J. Alpigini, James F. Peters, Andrzej Skowron, Ning Zhong, Editors, Third International Conference on Rough Sets and Current Trends in Computing RSCTC, volume 2475, Lecture Notes in Artificial Intelligence, pp. 397-404, Malvern, PA, October 14-16 2002. Springer-Verlag.
• [12] Kluska-Nawarecka S., Wilk-Kolodziejczyk D., Regulski, K. & Dobrowolski, G. (2011). Rough Sets Applied to the RoughCast System for Steel Castings, Intelligent Information and Database Systems, ACIIDS 2011, Pt II Volume 6592. 52-61.