Heuristic modeling of casting processes under the conditions uncertainty

• Autorzy: David J. , Švec P. , Garzinová R. , Kluska-Nawarecka S. , Wilk-Kołodziejczyk D. , Regulski K.

Identyfikatory

DOI

10.1016/j.acme.2015.10.006

• Języki publikacji: EN

Abstrakty

EN

In the first part of this paper will be described an analysis of control problems and technical lifetime modeling of continuous casting device crystallizers. A full exploitation of continuous casting equipment (CCE) advantages can only be achieved through a control system that minimizes all undesirable effects on the technological process. Some of the undesirable effects influencing the CCE process effectiveness are the failures and service interruptions. This problem was solved by connection of dependability theory and artificial neural networks. The second part of the article refers to a model in linguistic form used to identify the type of defects present in the tested casting. This model, having the form of an attribute table, has been based on the concepts taken from the theory of rough sets and fuzzy logic. A methodology for construction of a heuristic model of linguistic knowledge was presented along with an example of its implementation based on the use of distributed sources of knowledge.

Słowa kluczowe

EN

modeling; wear; artificial neural networks; identification of the defects in castings; attribute table

PL

modelowanie; sztuczne sieci neuronowe; odlew

• Wydawca: Springer ; Wrocław University of Science and Technology
• Czasopismo: Archives of Civil and Mechanical Engineering
• Rocznik: 2016
• Tom: Vol. 16, no. 2
• Strony: 179--185
• Opis fizyczny: Bibliogr. 14 poz., rys., tab., wykr.

Twórcy

autor

David J.

• Department of Automation and computing in Metallurgy, Vysoká škola báňská-Technical Univerzity of Ostrava, 17. listopadu 15, Ostrava-Poruba 708 00, Czech Republic

autor

Švec P.

• Department of Automation and computing in Metallurgy, Vysoká škola báňská-Technical Univerzity of Ostrava, 17. listopadu 15, Ostrava-Poruba 708 00, Czech Republic

autor

Garzinová R.

• Department of Automation and computing in Metallurgy, Vysoká škola báňská-Technical Univerzity of Ostrava, 17. listopadu 15, Ostrava-Poruba 708 00, Czech Republic

autor

Kluska-Nawarecka S.

• The Foundry Research Institute, Poland

autor

Wilk-Kołodziejczyk D.

• The Foundry Research Institute, Poland

autor

Regulski K.

• AGH University of Science and Technology, Poland

Bibliografia

• [1] J. David, et al., Stage 2 – Identification of diagnostics quantities and development of diagnostics system, Report about stage solving in TIP program, VŠB-TU, Ostrava, 2010/2012.
• [2] J. David, M. Heger, M. Vrozina, L. Valek, Visualization of data fields, Archives of Metallurgy and Materials 55 (3) (2010) 795– 801.
• [3] J. David, Z. Jancikova, R. Frischer, M. Vrozina, Crystallizer's desks surface diagnostics with usage of robotic system, Archives of Metallurgy and Materials 58 (3) (2013) 907–910.
• [4] O. Krejcar, R. Frischer, Non destructive defect detection by spectral density analysis, Sensors 11 (3) (2011) 2334–2346.
• [5] J. David, P. Svec, R. Frischer, M. Stranavova, Usage of rfid wireless identification technology to support decision making in steelworks, in: Proc. 21st International Conference on Metallurgy and Materials Metal, Brno, Czech Republic, (2012) 1734–1738.
• [6] M. Vrozina, et al., Usage of knowledge systems in maintenance control of metallurgical devices with help of continuous diagnostics into the solution, in: Final report of grant project 106/05/2596 for period 2005–2007, Ostrava, 2008.
• [7] P. Kostial, Z. Jancikova, D. Bakosova, J. Valicek, M. Harnicarova, I. Špicka, Artificial neural networks application in modal analysis of tires, Measurement Science Review 13 (5) (2013) 273–278.
• [8] O. Jancikova, M. Zimny, P. Kvicala, R.M. Kostial, Prediction of internal defects in rolled products from Cr-Mo steels using artificial intelligence methods, in: Proc. 22nd International Conference on Metallurgy and Materials Metal, Brno, Czech Republic, 2013.
• [9] A. Ligeza, Logical Foundations for Rule-Based Systems, Studies in Computional Intelligence, 2nd ed., AGH University of Science and Technology Press, Krakow, 2006, pp. 61–63.
• [10] Z. Górny, S. Kluska-Nawarecka, D. Wilk- Kołodziejczyk, Attribute-based knowledge representation in the process of defect diagnosis, Archives of Metallurgy and Materials 55 (3) (2010) 819–826.
• [11] S. Kluska-Nawarecka, B. Śnieżyński, W. Parada, M. Lustofin, D. Wilk-Kołodziejczyk, The use of LPR (logic of plausible reasoning) to obtain information on innovative casting technologies, Archives of Civil and Mechanical Engineering 14 (1) (2014) 25–31.
• [12] Z. Pawlak, Rough sets, decision algorithms and Bayes' theorem, European Journal of Operational Research 136 (1) (2002) 181–189.
• [13] S. Kluska-Nawarecka, G. Dobrowolski, R. Marcjan, Infocast – a system for quality control procedures and diagnosis of casting defects, Acta Metallurgica Slovaca 7 (3) (2001) 441–446.
• [14] J. Kacprzyk, Fuzzy Sets in Systems Analysis, PWN, Warsaw, 1986.

Uwagi

PL

Opracowanie ze środków MNiSW w ramach umowy 812/P-DUN/2016 na działalność upowszechniającą naukę