Wyniki wyszukiwania - BazTech

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

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

Archives of Foundry Engineering

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2023

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Vol. 23, iss. 4

22--33

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.

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Predicting the microstructure of compacted graphite iron using a fuzzy knowledge‑based system

Gumienny Grzegorz, Macioł Andrzej, Macioł Piotr

Archives of Civil and Mechanical Engineering

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2023

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Vol. 23, no. 2

art. no. e87, 2023

EN

One of the important engineering materials is compacted graphite iron (CGI). Obtaining an expected microstructure leading to desired material properties is relatively difficult. In this paper, we present an approach to predicting the microstructure with a fuzzy knowledge-based system. On the basis of the results of statistical analysis and expert knowledge, an original taxonomy of CGI casts was formulated. The procedure of data acquisition, specimen preparation, analysis procedure and microstructures obtained are presented. Methods for expert experience-supported knowledge extraction from experimental data, as well as methods for formalizing knowledge as fuzzy rules, are introduced. The proposed rulesets, the reasoning process, and exemplary results are provided. The verification results showed that, using our approach, it is possible to effectively predict the microstructure and properties of CGI casts even in the absence of sufficient data to use data-driven knowledge acquisition. On the basis of the results obtained, examples of possible applications of the developed approach are presented.

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Multistage discretization and clustering in multivariable classification of the impact of alloying elements on properties of hypoeutectic silumin

Regulski Krzysztof, Wilk-Kołodziejczyk Dorota, Kluska-Nawarecka Stanisława, Szymczak Tomasz, Gumienny Grzegorz, Jaśkowiec Krzysztof

Archives of Civil and Mechanical Engineering

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2019

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Vol. 19, no. 1

114--126

EN

The properties of hypoeutectic Al–Si alloy (silumin) with the addition of elements such as Cr, Mo, V and W are described. Changes in silumin microstructure under the impact of these elements result in a change of the mechanical properties. The research includes presentation of procedure for the acquisition of knowledge about these changes directly from experimental results using mixed data mining techniques. The procedure for analyzing small sets of experimental data for multistage, multivariate and multivariable models has been developed. Its use can greatly simplify such research in the future. An interesting achievement is the development of a voting procedure based on the results of classification trees and cluster analysis.