Effect of the Annealing Temperature on the Microstructure and Properties of Ausferritic Nodular Cast Iron

Gumienny, G.; Klimek, L.; Kurowska, B.

doi:10.1515/afe-2016-0047

Cited by 3 publications

(1 citation statement)

References 3 publications

(2 reference statements)

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“…There is also an alternative way to obtain the ausferritic matrix, i.e., by modifying the chemical composition of cast iron, using molybdenum together with copper or nickel [19][20][21]. The microstructure of ausferritic CGI is shown in Figure 1b.…”

Section: Introductionmentioning

confidence: 99%

Data-Driven Model Selection for Compacted Graphite Iron Microstructure Prediction

et al. 2022

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Compacted graphite iron (CGI), having a specific graphite form with a large matrix contact surface, is a unique casting material. This type of cast iron tends to favor direct ferritization and is characterized by a complex of very interesting properties. Intelligent computing tools such as artificial neural networks (ANNs) are used as predictive modeling tools, allowing their users to forecast the microstructure of the tested cast iron at the level of computer simulation. This paper presents the process of the development of a metamodel for the selection of a neural network appropriate for a specific chemical composition. Predefined models for the specific composition have better precision, and the initial selection provides the user with automation of reasoning and prediction. Automation of the prediction is based on the rules obtained from the decision tree, which classifies the type of microstructure. In turn, the type of microstructure was obtained by clustering objects of different chemical composition. The authors propose modeling the prediction of the volume fraction of phases in the CGI microstructure in a three-step procedure. In the first phase, k-means, unsupervised segmentation techniques were used to determine the metamodel (DT), which in the second phase enables the selection of the appropriate ANN submodel (third phase).

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Section: Introductionmentioning

confidence: 99%

Data-Driven Model Selection for Compacted Graphite Iron Microstructure Prediction

et al. 2022

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Application of Selected Artificial Intelligence Methods in a System Predicting the Microstructure of Compacted Graphite Iron

Mrzygłód

Gumienny

Wilk-Kołodziejczyk

et al. 2019

J. of Materi Eng and Perform

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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.

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Microstructure and Properties of the Nitrided Layer of Nodular Cast Iron

Liu

Zhuang

Liu

2019

Materials Performance and Characterization

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The plunger pair is an important part of the plunger pump. It is required to have high wear resistance, self-lubrication, deformation resistance, and sufficient strength and rigidity because of the harsh application environment, thus ductile iron is a common material for large piston pumps because of its excellent overall performance. In order to improve the friction and wear performance of spheroidal graphite cast iron plungers, a nitride layer was prepared on the ductile iron QT600-3 by the ion nitriding technique. The morphology of the permeable layer was observed with a metallographic microscope, and its hardness, friction, and wear properties were analyzed using a Vickers hardness tester and a multifunctional material tester. The results of the article show that the hardness of the nitrided specimens is greatly improved, which is about twice the hardness of the matrix. The friction coefficient is found to be much lower than the friction coefficient of the matrix; the diameter of the wear scar is small, showing a moderate change compared with the matrix; and the performance is about four times. Therefore, the ion nitriding technique can significantly improve the friction and wear resistance of the pump plunger pair.

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Effect of the Annealing Temperature on the Microstructure and Properties of Ausferritic Nodular Cast Iron

Cited by 3 publications

References 3 publications

Data-Driven Model Selection for Compacted Graphite Iron Microstructure Prediction

Data-Driven Model Selection for Compacted Graphite Iron Microstructure Prediction

Application of Selected Artificial Intelligence Methods in a System Predicting the Microstructure of Compacted Graphite Iron

Microstructure and Properties of the Nitrided Layer of Nodular Cast Iron

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