scholarly journals Data mining tools in identifying the components of the microstructure of compacted graphite iron based on the content of alloying elements

2017 ◽  
Vol 95 (9-12) ◽  
pp. 3127-3139 ◽  
Author(s):  
Dorota Wilk-Kolodziejczyk ◽  
Krzysztof Regulski ◽  
Grzegorz Gumienny ◽  
Barbara Kacprzyk ◽  
Stanislawa Kluska-Nawarecka ◽  
...  
2021 ◽  
Vol 62 (5) ◽  
pp. 675-679
Author(s):  
Ailong Jiang ◽  
Xuelei Tian ◽  
Hongda Song ◽  
Guili Gao ◽  
Qiang Wu ◽  
...  

2010 ◽  
Vol 457 ◽  
pp. 126-131
Author(s):  
Mathias König ◽  
Ingvar L. Svensson ◽  
Magnus Wessen

The influence of alloying elements on the chill formation in Compacted Graphite Iron (CGI) is investigated. Chill wedges cast in an industrial foundry were used to investigate the chill formation. A total number of 19 chemical compositions were studied, including three trials of varying nodularity treatment level; four trials of varying copper content; four trials of varying silicon content; four trials of varying tin content and four trials of varying carbide promoter content. Three wedges were cast for each alloy composition, of which one was used for measuring the temperature at three different heights in the wedge. Contrary to some previous reports, the results indicate that low-nodularity CGI is not more prone to chill formation (columnar white) than high-nodularity CGI. Trends regarding the effect of alloying elements on chill formation are shown to generally be in agreement with previous work on spheroidal graphite iron and lamellar graphite iron. Most of the samples also show carbide formation in centre line areas of the wedge (inverse chill), this occurrence is also discussed in the paper.


2017 ◽  
Vol 17 (3) ◽  
pp. 117-122 ◽  
Author(s):  
K. Regulski ◽  
D. Wilk-Kołodziejczyk ◽  
B. Kacprzyk ◽  
G. Gumienny ◽  
G. Rojek ◽  
...  

AbstractThis article presents the methodology for exploratory analysis of data from microstructural studies of compacted graphite iron to gain knowledge about the factors favouring the formation of ausferrite. The studies led to the development of rules to evaluate the content of ausferrite based on the chemical composition. Data mining methods have been used to generate regression models such as boosted trees, random forest, and piecewise regression models. The development of a stepwise regression modelling process on the iteratively limited sets enabled, on the one hand, the improvement of forecasting precision and, on the other, acquisition of deeper knowledge about the ausferrite formation. Repeated examination of the significance of the effect of various factors in different regression models has allowed identification of the most important variables influencing the ausferrite content in different ranges of the parameters variability.


Author(s):  
Bahar Dadashova ◽  
Chiara Silvestri-Dobrovolny ◽  
Jayveersinh Chauhan ◽  
Marcie Perez ◽  
Roger Bligh

2020 ◽  
Vol 28 ◽  
pp. 1286-1294
Author(s):  
Evangelia Nektaria Palkanoglou ◽  
Konstantinos P. Baxevanakis ◽  
Vadim V. Silberschmidt

2019 ◽  
Vol 32 (5-6) ◽  
pp. 243-251 ◽  
Author(s):  
Dongmei Xu ◽  
Guiquan Wang ◽  
Xiang Chen ◽  
Yanxiang Li ◽  
Yuan Liu ◽  
...  

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