Comparison of selected tools for generation of knowledge for foundry production

• Autorzy: Perzyk M. , Soroczyński A.
• Języki publikacji: EN

Abstrakty

EN

Two types of data mining tools, suitable for semi-automatic generation of knowledge in a form of logic rules, are presented in the paper: decision (classification) trees and rough sets theory algorithms. A comparative evaluation of rules obtained by these two methods, used for decision concerning application of feeders for grey iron castings, is performed. Data sets obtained as readouts form a semi-empirical nomograph of Holzmüller and Wlodawer were used for the testing. It was found that both methods lead to similar rules, which are also in agreement with the foundry practice. However, the decision trees were unable to provide some important and reliable rules, which were generated by the rough sets theory algorithm and they can also generate rules which are not supported by the training data.

Słowa kluczowe

EN

data mining; knowledge rules; decision tree; rough sets theory; foundry technology

PL

odlewnictwo; procesy odlewnicze; teoria zbiorów przybliżonych

• Wydawca: Komisja Odlewnictwa Polskiej Akademii Nauk Oddział w Katowicach
• Czasopismo: Archives of Foundry Engineering
• Rocznik: 2008
• Tom: Vol. 8, iss. 4
• Strony: 163--166
• Opis fizyczny: Bibliogr. 4 poz., rys., tab.

Twórcy

autor

Perzyk M.

autor

Soroczyński A.

• Warsaw University of Technology, Metal Casting Section, Narbutta 85, 02-524 Warszawa, Poland,

Bibliografia

• [1] A. Kusiak, C. Kurasek, Data mining of Printed-Cicuit Board Defects, IEEE Transactions on Robotics and Automation, vol. 17, No. 2 (2001) 191-196.
• [2] M. Perzyk, A. Soroczyński, R. Biernacki, Possibilities of decision trees applications for improvement of quality and economics of foundry production, Archives of Foundry Engineering, vol. 8, No. 1 (2008) 261-268.
• [3] A. Holzmüller, R. Wlodawer, Zehn Jahre Speiser-Einguss-Verfahren fur Gusseisen, Giesserei, vol. 50, No. 25 (1963) 781-791.
• [4] J. R. Quinlan, Simplifying decision trees, International Journal of Man-Machine Studies, vol. 27, No. 3 (1987) 221-234.