Temat: żeliwo sferoidalne - Katalog OPAC zbiorów

Skocz do pozycji: 1.

Tytuł:: The Importance of TDA Thermal Analysis in an Automated Metallurgical Process
Autorzy:: Petrus, Łukasz
Bulanowski, Andrzej
Kołakowski, Jakub
Sobieraj, Jakub
Paruch, Tomasz
Urbanowicz, Mariusz
Brzeżański, Mateusz
Burdzy, Daniel
Zych, Jerzy
Janerka, Krzysztof
Powiązania:: https://bibliotekanauki.pl/articles/2056035.pdf
Data publikacji:: 2021
Wydawca:: Akademia Górniczo-Hutnicza im. Stanisława Staszica w Krakowie. Wydawnictwo AGH
Tematy:: ductile iron
grey cast iron
thermal derivative analysis
melting automation
modification
żeliwo sferoidalne
żeliwo szare
termiczna analiza pochodnych
automatyzacja topienia
modyfikacja
Opis:: The article presents the results of research and work related to the implementation of the research and development project POIR.01.01.01-00-0120/17 co-financed by the EU, through the NCBR, entitled: Innovative technology using thermal analysis, TDA, of self-feeding manufacturing of high-quality cast iron to produce new generation, enhanced performance casts. In many foundries, thermal derivative analysis (TDA) is used in addition to chemical analysis to evaluate the physical and chemical properties of an alloy while it is still in the melting furnace or ladle and before it is poured into the mold. This fact makes it possible to improve the metallurgical quality of the alloy by introducing alloying additives, carburizers or modifiers into the furnace as part of the pre-modification or primary or secondary modification in the ladle or when pouring into molds. Foundry machinery (modifier dosing systems and spheroidizing station) is very important in these operations. Only the full synergy of modern equipment with modern technology ensures high quality and repeatability of the casting process. The article mainly discusses the obtained parameters of TDA analysis (with the use of the ITACA system) at different stages of melting and how to improve them by using modern and fully automated dosing systems (Itaca OptiDose, ItacaWire and ItacaStream). Special attention was paid to the minimum temperature of the eutectoid. The change of its value after the modification process, its influence on the quality of the melted metal, a very strong correlation with the number of nuclei and the number of graphite precipitations in the casts were shown.
Źródło:: Journal of Casting & Materials Engineering; 2021, 5, 4; 89--93
2543-9901
Pojawia się w:: Journal of Casting & Materials Engineering
Dostawca treści:: Biblioteka Nauki

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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

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