The Use of the XGBoost and Kriging Methods in the Prediction of the Microstructure of CGI Cast Iron

• Autorzy: Sztangret Łukasz , Olejarczyk-Wożeńska Izabela , Regulski Krzysztof , Gumienny Grzegorz , Mrzygłód Barbara

Identyfikatory

DOI

10.24425/afe.2023.146671

• Języki publikacji: EN

Abstrakty

EN

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.

Słowa kluczowe

EN

compacted graphite iron; CGI; microstructure; machine learning; artificial neural networks; Kriging; XGBoost

PL

żeliwo grafitowe; CGI; mikrostruktura; sztuczne sieci neuronowe; metoda Kriging; metoda XGBoost

• Wydawca: Komisja Odlewnictwa Polskiej Akademii Nauk Oddział w Katowicach
• Czasopismo: Archives of Foundry Engineering
• Rocznik: 2023
• Tom: Vol. 23, iss. 4
• Strony: 22--33
• Opis fizyczny: Bibliogr. 36 poz., rys., tab., wykr.

Twórcy

autor

Sztangret Łukasz

• AGH University of Science and Technology, Poland

• https://orcid.org/0000-0003-4872-406X

autor

Olejarczyk-Wożeńska Izabela

• AGH University of Science and Technology, Poland

• https://orcid.org/0000-0001-9838-4791

autor

Regulski Krzysztof

• AGH University of Science and Technology, Poland

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

autor

Gumienny Grzegorz

• Lodz University of Technology, Poland

• https://orcid.org/0000-0003-4671-3228

autor

Mrzygłód Barbara

• AGH University of Science and Technology, Poland

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

Bibliografia

• [1] König, M. (2010). Literature review of microstructure formation in compacted graphite iron. International Journal of Cast Metals Research. 23(3), 185-192. https://doi.org/10.1179/136404609X12535244328378.
• [2] Dawson, S. & Hang, F. (2009). Compacted graphite iron-a material solution for modern diesel engine cylinder blocks and heads. China Foundry. 6(3), 241-246.
• [3] Chen, Y., Pang, J. C., Li, S. X., Zou, C. L. & Zhang, Z. F. (2022). Damage mechanism and fatigue strength prediction of compacted graphite iron with different microstructures. International Journal of Fatigue. 164, 107126, 1-14. https://doi.org/10.1016/j.ijfatigue.2022.107126.
• [4] Sandoval, J., Ali, A., Kwon, P., Stephenson, D. & Guo, Y. (2023). Wear reduction mechanisms in modulated turning of compacted graphite iron with coated carbide tool. Tribology International. 178, 108062, 1-13. https://doi.org/10.1016/j.triboint.2022.108062.
• [5] Hosadyna-Kondracka, M., Major-Gabryś, K., Warmuzek, M. & Brůna, M. (2022). Quality assessment of castings manufactured in the technology of moulding sand with furfuryl-resole resin modified with PCL additive. Archives of Metallurgy and Materials. 67(2), 753-758. https://doi.org/10.24425/amm.2022.137814.
• [6] Mrzygłód, B., Łukaszek-Sołek, A., Olejarczyk-Wożeńska, I. & Pasierbiewicz, K. (2022). Modelling of plastic flow behaviour of metals in the hot deformation process using artificial intelligence methods. Archives of Foundry Engineering. 22(3), 41-52. DOI: 10.24425/afe.2022.140235.
• [7] Palkanoglou, E.N., Baxevanakis, K.P. & Silberschmidt, V.V. (2022). Thermal debonding of inclusions in compacted graphite iron: Effect of matrix phases. Engineering Failure Analysis. 139, 106476, 1-13. https://doi.org/10.1016/ j.engfailanal.2022.106476.
• [8] Patel, M., Dave, K. (2022). An insight of compacted graphite iron (CGI) characteristics and its production: a review. Recent Advances in Manufacturing Processes and Systems: Select Proceedings of RAM 2021, 131-148.
• [9] Górny, M., Lelito, J., Kawalec, M. & Sikora, G. (2015). Influence of structure on the thermophisical properties of thin walled castings. Archives of Foundry Engineering. 15(2), 23- 26.
• [10] Górny, M., Kawalec, M., Witek, G. & Rejek, A. (2019). The influence of wall thickness and mould temperature on structure and properties of thin wall ductile iron castings. Archives of Foundry Engineering. 19(2), 55-59. DOI: 10.24425/afe.2019.127116.
• [11] Saka, S.O., Seidu, S.O., Akinwekomi, A.D. & Oyetunji, A. (2021). Alloying elements variant on the development of antimony modified compacted graphite iron using rotary furnace. Annals of the Faculty of Engineering Hunedoara. 19(2), 13-22.
• [12] Soiński, M.S., Jakubus, A., Borowiecki, B. & Mierzwa, P. (2021). Initial assessment of graphite precipitates in vermicular cast iron in the as-cast state and after thermal treatments. Archives of Foundry Engineering. 21(4), 131-136.
• [13] Domeij, B., Elfsberg, J. & Diószegi, A. (2023). Evolution of dendritic austenite in parallel with eutectic in compacted graphite iron under three cooling conditions. Metallurgical and Materials Transactions B. 1-16.
• [14] Ren, Z., Jiang, H., Long, S. & Zou, Z. (2023). On the mechanical properties and thermal conductivity of compacted graphite cast iron with different pearlite contents. Journal of Materials Engineering and Performance. 1-9. https://doi.org/10.1007/s11665-023-07823-7.
• [15] Gumienny, G., Kacprzyk, B., Mrzygłód, B. & Regulski, K. (2022). Data-driven model selection for compacted graphite iron microstructure prediction. Coatings. 12(11), 1676, 1-18. DOI: 10.3390/coatings12111676.
• [16] Mrzygłód, B., Gumienny, G., Wilk-Kołodziejczyk, D. & Regulski, K., (2019). Application of selected artificial intelligence methods in a system predicting the microstructure of compacted graphite iron. Journal of Materials Engineering and Performance. 28, 3894-3904. DOI: 10.1007/s11665-019- 03932-4.
• [17] Wilk-Kołodziejczyk, D., Regulski, K., Gumienny, G. & Kacprzyk, B. (2018). Data mining tools in identifying the components of the microstructure of compacted graphite iron based on the content of alloying elements. International Journal of Advanced Manufacturing Technology. 95(9-12), 3127-3139. DOI 10.1007/s00170-017-1430-7.
• [18] Wilk-Kołodziejczyk, D., Kacprzyk, B., Gumienny, G., Regulski, K., Rojek, G. & Mrzygłód, B., (2017). Approximation of ausferrite content in the compacted graphite iron with the use of combined techniques of data mining, Archives of Foundry Engineering. 17(3), 117-122. DOI 10.1515/afe-2017-0102.
• [19] Kusiak, J., Sztangret, Ł. & Pietrzyk, M. (2015). Effective strategies of metamodelling of industrial metallurgical processes. Advances in Engineering Software. 89, 90-97. DOI: 10.1016/j.advengsoft.2015.02.002.
• [20] Sacks, J., Welch, W.J., Mitchel, T. & Wynn, H.P., (1989) Design and analysis of computer experiments. Stat Sci. 4, 409- 435. DOI: 10.1214/ss/1177012413.
• [21] Fragassa, C. (2022) Investigating the material properties of nodular cast iron from a data mining perspective. Metals. 12(9), 1493, 1-26. DOI: 10.3390/met12091493.
• [22] Huang, W., Lyu, Y., Du, M., Gao, S-D., Xu, R-J., Xia, Q-K. & Zhangzhou, J. (2022). Estimating ferric iron content in clinopy-roxene using machine learning models. American Mineralogist. 107, 1886-1900. DOI: 10.2138/am-2022-8189.
• [23] Sika, R., Szajewski, D., Hajkowski, J. & Popielarski, P. (2019). Application of instance-based learning for cast iron casting defects prediction. Management and Production Engineering Review. 10(4), 101-107. DOI: 10.24425/mper.2019.131450.
• [24] Chen, S. & Kaufmann, T. (2022). Development of data-driven machine learning models for the prediction of casting surface defects. Metals. 12(1), 1-15. DOI: 10.3390/met12010001
• [25] Alrfou, K., Kordijazi, A., Rohatgi, P. & Zhao, T. (2022). Synergy of unsupervised and supervised machine learning methods for the segmentation of the graphite particles in the microstructure of ductile iron. Materials Today Communications. 30. 103174. DOI: 10.1016/j.mtcomm.2022.103174.
• [26] Vantadori, S., Ronchei, C., Scorza, D., Zanichelli, A. & Luciano, R. (2022). Effect of the porosity on the fatigue strength of metals. Fatigue & Fracture of Engineering Materials & Structures. 45(9), 2734-2747. https://doi.org/10.1111/ffe.13783.
• [27] Dučić, N., Jovičić, A., Manasijević, S., Radiša, R., Ćojbašić, Z. & Savković, B. (2020). Application of machine learning in the control of metal melting production process. Applied Sciences. 10(17), 6048, 1-15. DOI: 10.3390/app10176048
• [28] Kihlberg, E., Norman, V., Skoglund, P., Schmidt, P. & Moverare, J. (2021). On the correlation between microstructural pa-rameters and the thermo-mechanical fatigue performance of cast iron. International Journal of Fatigue. 145, 106112, 1-10. DOI: 10.1016/j.ijfatigue.2020.106112.
• [29] Hernando, J.C., Elfsberg, J., Ghassemali, E., Dahle, A.K. & Diószegi, A. (2020). The role of primary austenite morphology in hypoeutectic compacted graphite iron alloys. International of Metalcasting. 14, 745-754. DOI: 10.1007/s40962-020-00410-9.
• [30] Regordosa, A., de la Torre, U., Loizaga, A., Sertucha, J. & Lacaze, J. (2020). Microstructure Changes During Solidification of Cast Irons: Effect of Chemical Composition and Inoculation on Competitive Spheroidal and Compacted Graphite Growth. International of Metalcasting. 14, 681-688. DOI: 10.1007/s40962-019-00389-y.
• [31] Ribeiro B.C.M., Rocha F.M., Andrade B.M., Lopes W., Corrêa E.C.S., (2020). Influence of different concentrations of silicon, copper and tin in the microstructure and in the mechanical properties of compacted graphite iron, Materials Research. 23(2), e2019-0678, 1-10. DOI: 10.1590/1980- 5373-MR-2019-0678.
• [32] Tan, P.-N., Steinbach, M. & Kumar, V. (2005). Introduction to Data Mining. Boston: Pearson Addison-Wesley.
• [33] Rokach, L. & Maimon, O. (2005). Top-down induction of decision trees classifiers-a survey. IEEE Transactions on Systems Man and Cybernetics Part C (Applications and Reviews). 35(4), 476-487.
• [34] Barros, R.C., de Carvalho, A. & Freitas, A.A. (2015). Automatic Design of Decision-Tree Induction Algorithms, Springer International Publishing.
• [35] Regulski, K., Wilk-Kołodziejczyk, D. & Gumienny, G. (2016). Comparative analysis of the properties of the Nodular Cast Iron with Carbides and the Austempered Ductile Iron with use of the machine learning and the support vector machine. The International Journal of Advanced Manufacturing Technology. 87(1), 1077-1093. DOI: 10.1007/s00170-016-8510-y.
• [36] Rui, G., Zhiqian, Z., Tao, W., Guangheng, L., Jingyi, Z. & Dianrong, G., (2020) Degradation state recognition of piston pump based on ICEEMDAN and XGBoost, Applied Sciences. 10(18), 6593, 1-17. DOI:10.3390/app10186593.

Uwagi

Opracowanie rekordu ze środków MNiSW, umowa nr SONP/SP/546092/2022 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2024)