ANN modelling for the determination of moulding sand matrix grain size

• Autorzy: Jakubski J. , Dobosz S. M. , Major-Gabryś K.
• Języki publikacji: EN

Abstrakty

EN

One of the modern methods of the production optimisation are artificial neural networks. Neural networks are gaining broader and broader application in the foundry industry, among others for controlling melting processes in cupolas and in arc furnaces, for designing castings and supply systems, for controlling moulding sand processing, for predicting properties of cast alloys or selecting parameters of pressure castings. An attempt to apply neural networks for controlling the quality of bentonite moulding sands is presented in this paper. This is the assessment method of sands suitability by means of detecting correlations between their individual parameters. The presented investigations were obtained by using the Statistica 9.0 program. The aim of the investigations was to select the neural network suitable for prediction the moulding sand matrix grain size on the basis of the determined sand properties such as: permeability, compactibility, and compressive strength.

Słowa kluczowe

EN

artificial neural networks; green moulding sands; sand grains

PL

sztuczna sieć neuronowa; syntetyczna masa formierska; osnowa piaskowa

• Wydawca: Komisja Odlewnictwa Polskiej Akademii Nauk Oddział w Katowicach
• Czasopismo: Archives of Foundry Engineering
• Rocznik: 2011
• Tom: Vol. 11, iss. 4
• Strony: 61--64
• Opis fizyczny: Bibliogr. 12 poz., wykr.

Twórcy

autor

Jakubski J.

autor

Dobosz S. M.

autor

Major-Gabryś K.

• AGH University of Science and Technology Faculty of Foundry Engineering, al. Mickiewicza 30, 30-059 Krakow,

Bibliografia

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• [2] J. Jakubski, St. M. Dobosz, K. Major-Gabryś, Influence of moulding sands grain size on the effectiveness of quality control systems. Archives of Foundry Engineering, 2011, vol. 11 iss. 2 s. 47-50.
• [3] Hüly a Kaçar Durmuş, Erdoğan Özkay a, Cevdet Meri Ç, The use of neural networks for the prediction of wear loss and surface roughness of AA 6351 aluminium alloy. Materials& Design. 2007, Vol. 27, pp. 156-159.
• [4] Mahesh B. Parappagoudar D. K. Pratihar, Datta G. L, Forward and reverse mappings in green sand mould system using neural networks. Applied Soft Computing. 2008, Vol. 8, pp. 239-260.
• [5] M. Perzy k, A. Kochański, Prediction of ductile cast iron quality by artificial neural networks. Journal of Material Processing Technology. 2001, Vol. 109, pp. 305-307.
• [6] J. Jakubski, St. M. Dobosz, The use of artificial neural networks for green moulding sands quality control. Transaction of the VŠB - Technical University of Ostrava Metallurgical Series. 2009, Vol. 2, pp. 109-114.
• [7] M. Perzy k, R. Biernacki, A. Kochański, Modeling of manufacturing p rocesses by learning systems: The naive Bayesian classifier versus artificial neural networks. Journal of Material Processing Technology. 2005, 164-165, 430- 1435.
• [8] J. Jakubski, St. M. Dobosz, Selected parameters of moulding sands for designing quality control systems. Archives of Foundry Engineering. 2010, Vol. 10, iss. 3, pp. 11-16.
• [9] J. Jakubski, St. M. Dobosz, The usage of data mining tools for green moulding sands quality control. Archives of Metallurgy and Materials. 2010, Vol. 55, iss. 3, pp. 843-849.
• [10] J. Jakubski, St. M. Dobosz, The use of artificial neural networks for rebonding of moulding sands.Technológ. 2010, Vol. 2, pp. 84-89.
• [11] J. Jakubski, St. M. Dobosz, K. Major-Gabryś, Active binder content as a factor of the control system of the moulding sand quality. Archives of Foundry Engineering, 2011, vol. 11 iss. 1 s. 49-52.
• [12] Statistica manual, StatSoft, Inc., 1984-2003.