The Prediction of Moulding Sand Moisture Content Based on the Knowledge Acquired by Data Mining Techniques

• Autorzy: Regulski K. , Jakubski J. , Opaliński A. , Brzeziński M. , Głowacki M.

Identyfikatory

DOI

10.1515/amm-2016-0277

• Języki publikacji: EN

Abstrakty

EN

The subject of the study is the improvement of the quality of moulding sand preparation. An exploration research performed on the data concerning moulding sand quality parameters was described. The aim of the research was to find relationships between various factors determining the properties of moulding sands and, based on the results obtained, build models predicting the sand moisture content with the induction of classification and regression trees. A two-match prediction approach was demonstrated and its effectiveness in evaluating the moulding sand moisture content was discussed. The knowledge in the form of rules acquired in this way can be used in the creation of knowledge bases for systems supporting decisions in the diagnostics of the moulding sand rebonding process. Formalized knowledge also facilitates further processing of the measurement data.

Słowa kluczowe

EN

application of information technology to the foundry industry; moulding sands; decision tree; ANOVA; data mining

• Wydawca: Institute of Metallurgy and Materials Science of Polish Academy of Sciences ; Committee of Materials Engineering and Metallurgy of Polish Academy of Sciences
• Czasopismo: Archives of Metallurgy and Materials
• Rocznik: 2016
• Tom: Vol. 61, iss. 3
• Strony: 1709--1714
• Opis fizyczny: Bibliogr. 27 poz., rys., schem., tab., wykr.

Twórcy

autor

Regulski K.

• AGH University of Science and Technology, Al. A. Mickiewicza 30, 30-059 Kraków, Poland

autor

Jakubski J.

• AGH University of Science and Technology, Al. A. Mickiewicza 30, 30-059 Kraków, Poland

autor

Opaliński A.

• AGH University of Science and Technology, Al. A. Mickiewicza 30, 30-059 Kraków, Poland

autor

Brzeziński M.

• AGH University of Science and Technology, Al. A. Mickiewicza 30, 30-059 Kraków, Poland

autor

Głowacki M.

• AGH University of Science and Technology, Al. A. Mickiewicza 30, 30-059 Kraków, Poland
• The Jan Kochanowski University (JKU), Kielce

Bibliografia

• [1] D. Hartmann, Process management and virtual engineering In foundries, Foundry – Science and Practice, 50, COCAFTEC, 2006, Foundry Research Institute, Cracow.
• [2] J. Jakubski, Artificial neural networks (ANN) as a tool for predicting the moulding sands properties in terms of supporting green moulding sand quality control, PhD thesis, AGH University of Science and Technology, Cracow 2012, Archives of Foundry Engineering, Katowice-Gliwice (2013).
• [3] J. Jakubski, St. M. Dobosz, Selected parameters of moulding sands for designing quality control systems, Archives of Foundry Engineering 10(3), 11–16 (2010)
• [4] P. Lewicki, T. Hill, Statistics: methods and applications, 2006, Tulsa, OK. Statsoft.
• [5] J. H. Friedman, Multivariate Adaptive Regression Splines, Ann. Statist. 19(1), 1-67 (1991).
• [6] K. Regulski, D. Szeliga, J. Kusiak, Data Exploration Approach Versus Sensitivity Analysis for Optimization of Metal Forming Processes, Key Engineering Materials 611–612, 1390–1395 (2014).
• [7] J. R. Quinlan, Induction of decision trees, Machine Learning, 1(1), 81-106 (1986).
• [8] L. Breiman, J. Friedman, C. J. Stone, R. A. Olshen, Classification and regression trees. 1984, CRC press.
• [9] N. Speybroeck, Classification and regression trees, International Journal of Public Health 57(1), 243-246 (2012).
• [10] G. V. Kass, An exploratory technique for investigating large quantities of categorical data, Applied Statistics, 119-127, (1980).
• [11] S. Kluska-Nawarecka, D. Wilk-Kołodziejczyk, K. Regulski, G. Dobrowolski, Rough sets applied to the RoughCast system for steel castings, Intelligent Information and Database Systems. Part II, Third International Conference, ACIIDS 2011, Daegu, Korea, April 20-22, 2011, Proceedings, Part II, Series: Springer Lecture Notes in Computer Science, Volume 6592/2011, 52-61, DOI: 10.1007/978-3-642-20042-7_6, Subseries: Lecture Notes in Artificial Intelligence, Nguyen, Ngoc Thanh; Kim, Chong-Gun; Janiak, Adam (Eds.), 1st Edition., 2011, XXVII, 580 p.
• [12] M. Warmuzek, K. Regulski, A Procedure for in situ Identification of the Intermetallic AITMSi Phase Precipitates in the Microstructure of the Aluminum Alloys, Praktische Metallographie-Practical Metallography 48, 12, 660-683 (2011).
• [13] J. David, P. Svec, R. Frischer, R. Garzinova, The Computer Support of Diagnostics of Circle Crystallizers; Metalurgija 53 (2):193-196; APR-JUN (2014).
• [14] J. Jakubski, St.M. Dobosz, The usage of data mining tools for green moulding sands quality control, Archives of Metallurgy and Materials 55(3), 843-849 (2010)
• [15] A. Glowacz, A. Glowacz, Z. Glowacz, Recognition of thermal images of direct current motor with application of area perimeter vector and bayes classifier, Measurement Science Review 15 (3), 119-126 (2015). DOI: 10.1515/msr-2015-0018
• [16] I. Olejarczyk-Wożeńska, A. Adrian, H. Adrian, B. Mrzygłód, Parametric representation of TTT diagrams of ADI cast iron, Archives of Metallurgy and Materials 57, 981-986 (2012), DOI: 10.2478/v10172-012-0065-9
• [17] A. Opalinski, W. Turek, K. Cetnarowicz, Scalable web monitoring system, in: Computer Science and Information Systems (FedCSIS), 2013 Federated Conference on. IEEE, 2013.
• [18] J. Jakubski, P. Malinowski, St. M. Dobosz, K. Major-Gabryś, ANN Modelling For The Analysis Of The Green Moulding Sands Properties, Archives of Metallurgy and Materials 58(3), 961-964 (2013)
• [19] P. Malinowski, J. S. Suchy, J. Jakubski, Technological knowledge management system for foundry industry, Archives of Metallurgy and Materials 58(3), 965-968 (2013)
• [20] K. Smyksy, E. Ziółkowski, R. Wrona, M. Brzeziński, Performance evaluation of rotary mixers through monitoring of power energy parameters, Archives of Metallurgy and Materials 58(3), 911-914 (2013)
• [21] Z. Ignaszak, R. Sika, System do eksploracji wybranych danych produkcyjnych oraz jego testowanie w odlewni. Archiwum Technologii Maszyn i Automatyzacji, 28(1), 61 – 72 (2008)
• [22] K. Bramczewski, M. Idee, S. Szwajkowski, System pomiaru i rejestracji temperatury zalewania form, instrukcja obsługi programu RTO PC Soft s.c, Piła 1996.
• [23] R. Sika, Studium nad strukturą systemu SAP R/3 i możliwości jego dostosowania do zarządzania oraz sterowania jakością w Odlewni Żeliwa ŚREM S.A., praca dyplomowa pod kierunkiem Z. Ignaszaka, Politechnika Poznańska, Wydział Budowy Maszyn i Zarządzania 2006.
• [24] R. Sika, Z. Ignaszak, Po wdrożeniu programu KonMas-final -jego wykorzystanie do analizy procesu produkcji odlewów na wydziale W6 - Odlewni Żeliwa ŚREM S.A., w: XI International Symposium - Modeling of casting and foundry processes, 26™ 27 October 2006, Poznań-Śrem (Poland).
• [25] B. Mahesh Parappagoudar, D. K. Pratihar, G. L. Datta, Forward and reverse mappings in green sand mould system using neural networks. Applied Soft Computing 8, 239-260 (2008)
• [26] M. Perzyk, A. W. Kochański, Prediction of ductile cast iron quality by artificial neural networks. Journal of Material Processing Technology 109, 305-307 (2001)
• [27] M. Perzyk, R. Biernacki, A. Kochański, Modeling of manufacturing processes by learning systems: The naïve Bayesian classifier versus artificial neural networks. Journal of Material Processing Technology 164–165, 430–1435 (2005)

Uwagi

EN

The work has been supported by the Polish Ministry of Science and Higher Education - AGH University of Science and Technology Funds No. 11.11.110.300.