@ARTICLE{Sztangret_Łukasz_The_2023,
 author={Sztangret, Łukasz and Olejarczyk-Wożeńska, Izabela and Regulski, Krzysztof and Gumienny, Grzegorz and Mrzygłód, Barbara},
 volume={vol. 23},
 number={No 4},
 pages={22-33},
 journal={Archives of Foundry Engineering},
 howpublished={online},
 year={2023},
 publisher={The Katowice Branch of the Polish Academy of Sciences},
 abstract={Compacted Graphite Iron (CGI), is a unique casting material characterized by its graphite form and extensive matrix contact surface. This type of cast iron has a tendency towards direct ferritization and possesses a complex set of intriguing properties. The use of data mining methods in modern foundry material development facilitates the achievement of improved product quality parameters. When designing a new product, it is always necessary to have a comprehensive understanding of the influence of alloying elements on the microstructure and consequently on the properties of the analyzed material. Empirical studies allow for a qualitative assessment of the above-mentioned relationships, but it is the use of intelligent computational techniques that allows for the construction of an approximate model of the microstructure and, consequently, precise predictions. The formulated prognostic model supports technological decisions during the casting design phase and is considered as the first step in the selection of the appropriate material type.},
 type={Article},
 title={The Use of the XGBoost and Kriging Methods in the Prediction of the Microstructure of CGI Cast Iron},
 URL={http://www.czasopisma.pan.pl/Content/129071/PDF-MASTER/AFE%204_2023_03-Final.pdf},
 doi={10.24425/afe.2023.146671},
 keywords={compacted graphite iron, Machine Learning, artificial neural networks, kriging, XGBoost},
}