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Wyświetlanie 1-3 z 3
Tytuł:
Initial Assessment of Graphite Precipitates in Vermicular Cast Iron in the As-Cast State and after Thermal Treatments
Autorzy:
Soiński, M. S.
Jakubus, Aneta
Borowiecki, B.
Mierzwa, P.
Powiązania:
https://bibliotekanauki.pl/articles/2126913.pdf
Data publikacji:
2021
Wydawca:
Polska Akademia Nauk. Czytelnia Czasopism PAN
Tematy:
vermicular cast iron
graphite precipitates
thermal treatments
austempering
żeliwo wermikularne
morfologia grafitu
obróbka termiczna
hartowanie
Opis:
The purpose of the work was to determine the morphology of graphite that occurs in vermicular cast iron, both in the as-cast state and after heat treatment including austenitization (held at a temperature of 890°C or 960°C for 90 or 150 min) and isothermal quenching (i.e. austempering, at a temperature of 290°C or 390°C for 90 or 150 min). In this case, the aim here was to investigate whether the heat treatment performed, in addition to the undisputed influence of the cast iron matrix on the formation of austenite and ferrite, also affects the morphology of the vermicular graphite precipitates and to what extent. The investigations were carried out for the specimens cut from test coupons cast in the shape of an inverted U letter (type IIb according to the applicable standard); they were taken from the 25mm thick walls of their test parts. The morphology of graphite precipitates in cast iron was investigated using a Metaplan 2 metallographic microscope and a Quantimet 570 Color image analyzer. The shape factor F was calculated as the quotient of the area of given graphite precipitation and the square of its perimeter. The degree of vermicularization of graphite was determined as the ratio of the sum of the graphite surface and precipitates with F <0.05 to the total area occupied by all the precipitations of the graphite surface. The examinations performed revealed that all the heat-treated samples made of vermicular graphite exhibited the lower degree of vermicularization of the graphite compared to the corresponding samples in the as-cast state (the structure contains a greater fraction of the nodular or nearly nodular precipitates). Heat treatment also caused a reduction in the average size of graphite precipitates, which was about 225μm2 for the as-cast state, and dropped to approximately 170-200 μm2 after the austenitization and austempering processes.
Źródło:
Archives of Foundry Engineering; 2021, 21, 4; 131--136
1897-3310
2299-2944
Pojawia się w:
Archives of Foundry Engineering
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Artificial Intelligence Approaches to Determine Graphite Nodularity in Ductile Iron
Autorzy:
Brait, Maximilian
Koppensteiner, Eduard
Schindelbacher, Gerhard
Li, Jiehua
Schumacher, Peter
Powiązania:
https://bibliotekanauki.pl/articles/2056034.pdf
Data publikacji:
2021
Wydawca:
Akademia Górniczo-Hutnicza im. Stanisława Staszica w Krakowie. Wydawnictwo AGH
Tematy:
ductile iron
graphite nodularity
graphite morphology
artificial intelligence
machine learning
żeliwo sferoidalne
guzkowatość grafitu
morfologia grafitu
sztuczna inteligencja
uczenie maszynowe
Opis:
The complex metallurgical interrelationships in the production of ductile cast iron can lead to enormous differences in graphite formation and local microstructure by small variations during production. Artificial intelligence algorithms were used to describe graphite formation, which is influenced by a variety of metallurgical parameters. Moreover, complex physical relationships in the formation of graphite morphology are also controlled by boundary conditions of processing, the effect of which can hardly be assessed in everyday foundry operations. The influence of relevant input parameters can be predetermined using artificial intelligence based on conditions and patterns that occur simultaneously. By predicting the local graphite formation, measures to stabilise production were defined and thereby the accuracy of structure simulations improved. In course of this work, the most important dominating variables, from initial charging to final casting, were compiled and analysed with the help of statistical regression methods to predict the nodularity of graphite spheres. We compared the accuracy of the prediction by using Linear Regression, Gaussian Process Regression, Regression Trees, Boosted Trees, Support Vector Machines, Shallow Neural Networks and Deep Neural Networks. As input parameters we used 45 characteristics of the production process consisting of the basic information including the composition of the charge, the overheating time, the type of melting vessel, the type of the inoculant, the fading, and the solidification time. Additionally, the data of several thermal analysis, oxygen activity measurements and the final chemical analysis were included. Initial programme designs using machine learning algorithms based on neural networks achieved encouraging results. To improve the degree of accuracy, this algorithm was subsequently adapted and refined for the nodularity of graphite.
Źródło:
Journal of Casting & Materials Engineering; 2021, 5, 4; 94--102
2543-9901
Pojawia się w:
Journal of Casting & Materials Engineering
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Influence of Structure on the Thermophisical Properties of Thin Walled Castings
Autorzy:
Górny, M.
Lelito, J.
Kawalec, M.
Sikora, G.
Powiązania:
https://bibliotekanauki.pl/articles/382519.pdf
Data publikacji:
2015
Wydawca:
Polska Akademia Nauk. Czytelnia Czasopism PAN
Tematy:
solidification process
thin walled casting
graphite morphology
thermal diffusivity
specific heat
thermal conductivity
proces krzepnięcia
odlew cienkościenny
morfologia grafitu
dyfuzyjność termiczna
ciepło właściwe
przewodność cieplna
Opis:
This study addresses the effect of the cooling rate and of titanium additions on the thermophysical parameters of thin-walled compacted graphite iron (TWCGI) castings. Various molding materials were used (silica sand and insulating sand LDASC- Low-Density Alumina-Silicate Ceramic) to achieve different cooling rates. Different titanium additions were caused by various amount of Ferro Titanium. The research work was conducted for thin-walled iron castings with a 3-mm wall thickness. The tested material represents the occurrence of graphite in the shape of flakes (C and D types, according to the ISO Standard), nodules or compacted graphite with a percent of nodularity and different shape factor. Thermal conductivity has been determined by the laser flash technique in a temperature range of 22-600°C. The results show that the cooling rates together with the titanium content largely influence the graphite morphology and finally thermal conductivity of thin walled iron castings.
Źródło:
Archives of Foundry Engineering; 2015, 15, 2 spec.; 23-26
1897-3310
2299-2944
Pojawia się w:
Archives of Foundry Engineering
Dostawca treści:
Biblioteka Nauki
Artykuł
    Wyświetlanie 1-3 z 3

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