Preparation Vermicular Graphite in Thin and Thick Wall Iron Castings

Kopyciński, D.; Guzik, E.; Nowak, Andrzej; Ronduda, M.; Sokolnicki, M.

doi:10.2478/v10266-012-0033-x

Cited by 3 publications

(2 citation statements)

References 4 publications

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“…To obtain pearlite, copper, nickel, or tin are used. Due to the unique properties of CGI, the technology is still being developed, which is reflected in many publications [2][3][4][5][6][7][8][9][10][11]. There is a lot of information on the effect of alloy additions on the microstructure and the properties of CGI, e.g., [12][13][14].…”

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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“…Currently, this material is mainly used for the construction of IC engine blocks, manifolds, and similar parts. Very interesting complex of properties offered by this cast iron is the object of intensive research and numerous publications [2][3][4][5][6][7][8][9][10][11][12]. There is a fairly large group of publications describing the possibility of obtaining ausferrite in the vermicular graphite cast iron, giving rise to the cast iron grades known as AVI or CAVI [13][14][15][16][17].…”

Section: Introductionmentioning

confidence: 99%

Effect of Chromium on the Solidification Process and Microstructure of Vermicular Graphite Cast Iron

Gumienny

Dondzbach

Kacprzyk

2015

Archives of Foundry Engineering

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The paper presents the results of studies of the effect of chromium concentration on the solidification process, microstructure and selected properties of cast iron with vermicular graphite. The vermicular graphite cast iron was obtained by an Inmold process. Studies covered the cast iron containing chromium in a concentration at which graphite is still able to preserve its vermicular form. The effect of chromium on the temperature of eutectic crystallization and on the temperature of the start and end of austenite transformation was discussed. The conditions under which, at a predetermined chromium concentration, the vermicular graphite cast iron of a pearlitic matrix is obtained were presented, and the limit concentration of chromium was calculated starting from which partial solidification of the cast iron in a metastable system takes place. The effect of chromium on the hardness of cast iron, microhardness of individual phases and surface fraction of carbides was disclosed.

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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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Preparation Vermicular Graphite in Thin and Thick Wall Iron Castings

Cited by 3 publications

References 4 publications

Data-Driven Model Selection for Compacted Graphite Iron Microstructure Prediction

Data-Driven Model Selection for Compacted Graphite Iron Microstructure Prediction

Effect of Chromium on the Solidification Process and Microstructure of Vermicular Graphite Cast Iron

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

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