The influence of changes in active binder content on the control system of the moulding sand quality

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

Abstrakty

EN

Artificial neural networks are one of the modern methods of the production optimisation. 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 aimed at the selection of the neural network able to predict the active bentonite content in the moulding sand on the basis of this sand properties such as: permeability, compactibility and the compressive strength. Then, the data of selected parameters of new moulding sand were set to selected artificial neural network models. This was made to test the universality of the model in relation to other moulding sands. An application of the Statistica program allowed to select automatically the type of network proper for the representation of dependencies occurring in between the proposed moulding sand parameters. The most advantageous conditions were obtained for the uni-directional multi-layer perception (MLP) network. Knowledge of the neural network sensitivity to individual moulding sand parameters, allowed to eliminate not essential ones.

Słowa kluczowe

EN

quality management; green moulding sands; artificial neural networks

PL

zarządzanie jakością; masa formierska; sztuczna sieć neuronowa

• Wydawca: Komisja Odlewnictwa Polskiej Akademii Nauk Oddział w Katowicach
• Czasopismo: Archives of Foundry Engineering
• Rocznik: 2012
• Tom: Vol. 12, iss. 4
• Strony: 71--74
• Opis fizyczny: Bibliogr. 12 poz., tab., 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, Poland,

Bibliografia

• [1] Perzyk M., Maciejak S., Kozłowski J. (2011). Application of time-series analysis for prediction of molding sand properties in production cycle. Archives of Foundry Engineering. Volume. 11 issue 2 pp. 95-100.
• [2] Sika R., Ignaszak Z. (2011). Data acquisition in modeling using neural networks and decision trees. Archives of Foundry Engineering. Volume. 11 issue 2 pp. 113-122.
• [3] Ignaszak Z., Sika R. (2008) The system to explore the chosen production data and its testing in the foundry. Archives of Mechanical Technology and Automation. Volume 28. pp. 61-72 (in Polish).
• [4] Hülya Kaçar Durmuş, Erdoğan Özkaya, Cevdet Meri Ç. (2007). The use of neural networks for the prediction of wear loss and surface roughness of AA 6351 aluminium alloy. Materials& Design. Volume. 27, pp. 156-159. DOI: 10.1016/j.matdes.2004.09.011.
• [5] Mahesh B. Parappagoudar D. K. Pratihar, Datta G. L. (2008). Forward and reverse mappings in green sand mould system using neural networks. Applied Soft Computing. Volume 8. pp. 239-260. DOI: 10.1016/j.asoc.2007.01.005.
• [6] Perzyk M., Biernacki R., Kochański A. (2005). Modeling of manufacturing processes by learning systems: The naive Bayesian classifier versus artificial neural networks. Journal of Material Processing Technology., pp. 164-165. DOI: 10.1016/j.jmatprotec.2005.02.043.
• [7] Rudy Cz. Preparing and rebonding of moulding sand. Retrieved June 11, 2012, from http://www.technical.com.pl/files/exposition/Konferencje_IX_Konferencja_Odlewnicza_Technical_xxaec.pdf.
• [8] Dobosz St. M. (1986). Compactability and rebounding of moulding sand problems. 12th Foundryman Day Scientific Symposium, pp. 221-233 (in Polish).
• [9] Jakubski J., Dobosz St. M. (2010) Selected parameters of moulding sands for designing quality control systems. Archives of Foundry Engineering., Volume 10, issue 3, pp. 11-16.
• [10] Jakubski J., Dobosz St. M. (2010). The usage of data mining tools for green moulding sands quality control. Archives of Metallurgy and Materials. 2010, Volume 55, issue 3, pp. 843-849.
• [11] Jakubski J., Dobosz St. M. (2010). The use of artificial neural networks for rebonding of moulding sands. Technológ. 2010, Volume 2, pp. 84-89.
• [12] Jakubski J., Dobosz St. M., Major-Gabryś K. (2011). Active binder content as a factor of the control system of the moulding sand quality. Archives of Foundry Engineering. Volume. 11 issue 1 pp. 49-52.