Artificial Neural Networks as a Tool for Supporting a Moulding Sand Control System Based on the Dependency between Selected Moulding Sand Properties

• Autorzy: Mrzygłód Barbara , Jakubski Jarosław , Opaliński Andrzej , Regulski Krzysztof

Identyfikatory

DOI

10.7494/jcme.2023.7.2.15

• Języki publikacji: EN

Abstrakty

EN

The article presents the potential for using artificial neural networks to support decisions related to the rebonding of green moulding sand. The basic properties of the moulding sand tested in foundries are discussed, especially compactibility as it gives the most information about the quality of green moulding sand. First, the data that can predict the compactibility value without the need for testing are defined. Next, a method for constructing an artificial neural network is presented and the network model which produced the best results is analysed. Additionally, two applications were designed to allow the investigation results to be searchable by determining the range of values of the moulding sand parameters.

Słowa kluczowe

EN

artificial neural network; decision support; green moulding sand; compactibility

• Wydawca: AGH University of Science and Technology Press
• Czasopismo: Journal of Casting & Materials Engineering
• Rocznik: 2023
• Tom: Vol. 7, no. 2
• Strony: 15--21
• Opis fizyczny: Bibliogr. 18 poz., rys., wykr.

Twórcy

autor

Mrzygłód Barbara

• AGH University of Krakow, Al. A. Mickiewicza 30, 30-059 Krakow, Poland

• https://orcid.org/0000-0001-9619-5305

autor

Jakubski Jarosław

• AGH University of Krakow, Al. A. Mickiewicza 30, 30-059 Krakow, Poland

• https://orcid.org/0000-0001-8767-2907

autor

Opaliński Andrzej

• AGH University of Krakow, Al. A. Mickiewicza 30, 30-059 Krakow, Poland

• https://orcid.org/0000-0002-9730-9594

autor

Regulski Krzysztof

• AGH University of Krakow, Al. A. Mickiewicza 30, 30-059 Krakow, Poland

• https://orcid.org/0000-0001-8080-2254

Bibliografia

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• Tompos A., Margitfalvi J.L., Tfirst E. & Héberger K. (2007). Predictive performance of “highly complex” artificial neural networks. Applied Catalysis A: General, 324(1), 90–93. Doi: https://doi.org/10.1016/j.apcata.2007.02.052.
• Nielsen M.A. (2015). Neural networks and deep learning, vol. 25. San Francisco, CA: Determination Press.
• Magalhaes B.R.C., Sterling T., Hines M. & Schurmann F. (2019). Fully-Asynchronous Cache-Efficient Simulation of Detailed Neural Networks. In: Rodrigues J., Cardoso J.S., Monteiro J., Lam R., Krzhizhanovskaya V.V., Lees M.H., Dongarra J.J., Sloot P.M.A. (Eds.) Computational Science – ICCS 2019. 19th International Conference, Faro, Portugal, June 12–14, 2019, Proceedings, Part III, vol. 11538. Springer, Cham. Doi: https://doi.org/10.1007/978-3-030-22744-9 33.
• Tadeusiewicz R. (2015). Neural networks in mining sciences – general overview and some representative examples. Archives of Mining Sciences, 60(4), 971–984. Doi: https://doi.org/10.1515/amsc-2015-0064.
• Jakubski J., Malinowski P., Dobosz S.M. & Major-Gabryś K. (2013). ANN modelling for the analysis of the green moulding sands properties. Archives of Metallurgy and Materials, 58(3), 961–963. Doi: https://doi.org/10.2478/amm-2013-0110.
• Regulski K., Jakubski J., Opaliński A., Brzeziński M. & Głowacki M. (2010). The prediction of moulding sand moisture content based on the knowledge acquired by data mining techniques. Archives of Metallurgy and Materials, 61(3), 1709–1714. Doi: https://doi.org/10.1515/amm-2016-0277.
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Uwagi

PL

Opracowanie rekordu ze środków MEiN, umowa nr SONP/SP/546092/2022 w ramach programu „Społeczna odpowiedzialność nauki” - moduł: Popularyzacja nauki i promocja sportu (2022-2023)