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

Authors

  • Barbara Mrzygłód AGH University of Krakow, Al. A. Mickiewicza 30, 30-059 Krakow, Poland
  • Jarosław Jakubski AGH University of Krakow, Al. A. Mickiewicza 30, 30-059 Krakow, Poland https://orcid.org/0000-0001-8767-2907
  • Andrzej Opaliński AGH University of Krakow, Al. A. Mickiewicza 30, 30-059 Krakow, Poland https://orcid.org/0000-0002-9730-9594
  • Krzysztof Regulski AGH University of Krakow, Al. A. Mickiewicza 30, 30-059 Krakow, Poland https://orcid.org/0000-0001-8080-2254

DOI:

https://doi.org/10.7494/jcme.2023.7.2.15

Abstract

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.

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References

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Published

2023-05-22

How to Cite

Mrzygłód, B., Jakubski, J., Opaliński, A., & Regulski, K. (2023). Artificial Neural Networks as a Tool for Supporting a Moulding Sand Control System Based on the Dependency between Selected Moulding Sand Properties. Journal of Casting &Amp; Materials Engineering, 7(2), 15–21. https://doi.org/10.7494/jcme.2023.7.2.15

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