Possibilities of decision trees applications for improvement of quality and economics of foundry production

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

Abstrakty

EN

The key activity areas, related to quality and economics of foundry production, are presented: designing of manufacturing processes, control of production processes as well as analysis of root causes of process faults and irregularities, are presented. Possibilities of utilization of data mining methods, including decision (classification) trees type learning systems, are indicated. In particular, the role of that kind of tools in decision making concerning selection of process type and optimum materials and parameters as well as in identification of process excessive variations, similarly like with the control charts, are discussed. Evaluation results of classification systems form the viewpoint of their applicability, accuracy and software availability are presented, including decision trees, naïve Bayesian classifier, rough sets theory, direct rule induction methods as well as artificial neural networks. In the final part of the paper a knowledge in the form rules obtained form classification trees is demonstrated using the example of decision making related to application of risers for grey cast iron castings.

Słowa kluczowe

EN

application of information technology; foundry industry; quality management; data mining; decision tree; knowledge rules

PL

zastosowanie technologii informatycznych; przemysł odlewniczy; zarządzanie jakością; eksploracja danych; drzewo decyzyjne; reguły wiedzy

• Wydawca: Komisja Odlewnictwa Polskiej Akademii Nauk Oddział w Katowicach
• Czasopismo: Archives of Foundry Engineering
• Rocznik: 2008
• Tom: Vol. 8, iss. 1 spec.
• Strony: 261--268
• Opis fizyczny: Bibliogr. 12 poz., rys., tab., wykr.

Twórcy

autor

Perzyk M.

• Metal Casting Department, Warsaw University of Technology, ul. Narbutta 85, 02-524 Warszawa, Poland

autor

Soroczyński A.

• Metal Casting Department, Warsaw University of Technology, ul. Narbutta 85, 02-524 Warszawa, Poland

autor

Biernacki R.

• Metal Casting Department, Warsaw University of Technology, ul. Narbutta 85, 02-524 Warszawa, Poland

Bibliografia

• [1] A. Kusiak, Data mining: manufacturing and service applications, International Journal of Production Research, vol. 44, No. 18-19 (2006) 4175-4191.
• [2] J.A. Harding, M. Shahbaz, Srinivas and A. Kusiak, Data mining in manufacturing: A review, J Manuf Sci Eng Trans ASME, vol. 128, No. 4 (2006) 969-976.
• [3] K. Wang, Applying data mining to manufacturing: The nature and implications, J Intell Manuf, vol. 18 No. 4 (2007) 487-495.
• [4] M. Perzyk, Data mining in foundry production, Research in Polish Metallurgy at the Beginning of XXI Century, Committee of Metallurgy of the Polish Academy of Sciences, ed. K. Świątkowski, Kraków, 2006.
• [5] M. Perzyk, R. Biernacki and J. Kozłowski, Data mining in manufacturing: methods, potentials, limitations, Advances in Production Engineering conference, Warsaw University of Technology, Poland, 13-16 June 2007, 147-156 (Publishing and Printing House of the Institute for Sustainable Technologies-NRI, Radom, Poland).
• [6] StatSoft, Inc. (2007). Electronic Statistics Textbook. Tulsa, OK: StatSoft. WEB: http://www.statsoft.com/textbook/stathome.html.
• [7] X. Guo, Implementing Six Sigma in Foundry Industry, AFS Transactions, vol. 110 (2002), 199-210.
• [8] S. Kannan, J. E. Thixton, System Approach to Casting Defect Analyses and Reduction: Hydrogen Gas Defect in Iron Castings, AFS Transactions, vol. 112 (2004), 115-119.
• [9] P.L. Barker, B. Bidassie, Using Statistical Tools to Detect and Improve Core Shift: A Case Study, AFS Transactions, vol. 112(2004), 121-130.
• [10] M. Perzyk, J. Kozłowski, Comparison of statistical and neural networks-based methods in analysis of significance and interaction of manufacturing processes parameters, Computer Methods in Materials Science, vol. 6, No. 2 , 81-93.
• [11] A. Holzmüller, R. Wlodawer, Zehn Jahre Speiser-Einguss-Verfahren fur Gusseisen, Giesserci, vol. 50, No. 25 (1963) 781-791.
• [12] J. R. Quinlan, Simplifying decision trees, International Journal of Man-Machine Studies, vol. 27, No. 3 (1987) 221-234.