Influence of moulding sands grain size on the effectiveness of quality control systems

• 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 owe their popularity to the fact that they constitute convenient tools, which can be applied in an extremely broad research scope. This is caused by their ability to represent complex functions. Their non-linearity should be specially emphasised. 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 moisture on the basis of the determined sand properties such as: permeability, compactibility, friability and compressive strength in dependence on the matrix grain size.

Słowa kluczowe

EN

quality management; green sand; artificial neural networks

PL

zarządzanie jakością; piasek świeży; piasek odlewniczy; sztuczna sieć neuronowa

• Wydawca: Komisja Odlewnictwa Polskiej Akademii Nauk Oddział w Katowicach
• Czasopismo: Archives of Foundry Engineering
• Rocznik: 2011
• Tom: Vol. 11, iss. 2
• Strony: 47--50
• Opis fizyczny: Bibliogr. 12 poz., wykr.

Twórcy

autor

Jakubski J.

autor

Dobosz S. M.

autor

Major-Gabryś K.

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

Bibliografia

• [1] Z. Ignaszak, R. Sika, The system to explore the chosen production data and its testing in the foundry. Archives of Mechanical Technology and Automation.. 2008, Vol. 28, 1, pp. 61-72 (in Polish).
• [2] Hülya Kaçar Durmuş, Erdoğan Özkaya, 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.
• [3] B. Mahesh, D. K. Parappagoudar, Pratihar, G. L. Datta, Forward and reverse mappings in green sand mould system using neural networks. Applied Soft Computing. 2008, Vol. 8, pp. 239-260.
• [4] M. Perzyk, A. Kochański, Prediction of ductile cast iron quality by artificial neural networks. Journal of Material Processing Technology. 2001, Vol. 109, pp. 305-307.
• [5] 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.
• [6] 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. 2005, 164-165, 430-1435.
• [7] Statistica - electronic textbook, StatSoft, Inc., 1984-2003.
• [8] http://www.technical.com.pl/files/exposition/Konferencje_IX_Konferencja_Odlewnicza_Technical_xxaec.pdf.
• [9] 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.
• [10] 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.
• [11] J. Jakubski, St. M. Dobosz, The use of artificial neural networks for rebonding of moulding sands.Technológ. 2010, Vol. 2, pp. 84-89.
• [12] J. L. Lewandowski, Materials for moulds, Akapit 1997 (in Polish).