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Predicting the microstructure of compacted graphite iron using a fuzzy knowledge-based system
https://orcid.org/0000-0003-4671-3228
https://orcid.org/0000-0002-1354-6489
https://orcid.org/0000-0002-3733-3509
Abstract 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.
Predicting the microstructure of compacted graphite iron using a fuzzy knowledge-based system
https://orcid.org/0000-0003-4671-3228
https://orcid.org/0000-0002-1354-6489
https://orcid.org/0000-0002-3733-3509
Abstract 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.
Predicting the microstructure of compacted graphite iron using a fuzzy knowledge-based system
https://orcid.org/0000-0003-4671-3228
https://orcid.org/0000-0002-1354-6489
https://orcid.org/0000-0002-3733-3509
Abstract 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.
Predicting the microstructure of compacted graphite iron using a fuzzy knowledge-based system
Archiv.Civ.Mech.Eng
Gumienny, Grzegorz (author) / Macioł, Andrzej (author) / Macioł, Piotr (author)
2023-02-28
Article (Journal)
Electronic Resource
English
Predicting the microstructure of compacted graphite iron using a fuzzy knowledge-based system
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