Application of Selected Artificial Intelligence Methods in a System Predicting the Microstructure of Compacted Graphite
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Application of Selected Artificial Intelligence Methods in a System Predicting the Microstructure of Compacted Graphite Iron Barbara Mrzygło´d
, Grzegorz Gumienny, Dorota Wilk-Kołodziejczyk, and Krzysztof Regulski
(Submitted September 28, 2018; in revised form December 29, 2018; published online February 19, 2019) Intelligent computing tools such as artificial neural network and fuzzy logic are used as predictive modeling tools. The use of these methods, combined with model experimental results, may be an excellent predictive tool, allowing us to forecast the microstructure of the tested cast iron at the level of computer simulation. In this study, the reference training cases collected in one database were used to determine the parameters of the neuro-fuzzy ANFIS model. They mainly include the results of observations and measurements of the content of individual microstructural constituents of the compacted graphite iron, examined as a function of the content of individual alloy additives (molybdenum, nickel and copper introduced in various proportions). The training process of such a fuzzy inference system is done by constantly changing its parameters (parameters of the membership function) and determining new rule conclusions as a result of presenting individual case examples from the training sample. The conducted research has shown the possibility of applying the ANFIS model as a tool to control the chemical composition of compacted graphite iron in the production of castings with high-strength parameters. Keywords
ANFIS, compacted graphite iron, data mining, foundry industry, information technology
1. Introduction Compacted graphite iron (CGI) having a specific graphite morphology with a large contact surface with the matrix is a unique casting material. It is characterized by a tensile strength range of 300-500 MPa at corresponding elongation of 2-0.5%, respectively. Compared to gray cast iron, this material demonstrates higher strength properties and higher elongation. The matrix microstructure is less dependent on the casting wall thickness. In comparison with spheroidal graphite cast iron, cast iron with compacted graphite exhibits lower coefficient of thermal expansion, higher thermal conductivity, better resistance to thermal shocks, higher vibration damping capacity and better castability. The representative microstructure of the spheroidal graphite cast iron as well as CGI is presented in Fig. 1(a) and (b).
This article is an invited submission to JMEP selected from presentations at the 73rd World Foundry Congress and has been expanded from the original presentation. 73WFC was held in Krakow, Poland, September 23-27, 2018, and was organized by the World Foundry Organization and Polish FoundrymenÕs Association. Barbara Mrzygło´d, Dorota Wilk-Kołodziejczyk, and Krzysztof Regulski, Department of Applied Computer Science and Modelling, AGH University of Science and Technology, Al. Mickiewicza 30, 30-059 Krako´w, Poland; and Grzegorz Gumienny, Politechnika Lodzka, Wydzial M
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