Artificial Intelligence Approaches to Determine Graphite Nodularity in Ductile Iron

Brait, Maximilian; Koppensteiner, Eduard; Schindelbacher, Gerhard; Li, Jiehua; Schumacher, Peter

doi:10.7494/jcme.2021.5.4.94

Artykuł - szczegóły

Tytuł artykułu

Artificial Intelligence Approaches to Determine Graphite Nodularity in Ductile Iron

Autorzy

Brait Maximilian , Koppensteiner Eduard , Schindelbacher Gerhard , Li Jiehua , Schumacher Peter

Treść / Zawartość

Pełne teksty:

4511-Article Text-20287-1-10-20211221.pdf

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DOI

10.7494/jcme.2021.5.4.94

Warianty tytułu

Języki publikacji

Abstrakty

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.

Słowa kluczowe

ductile iron graphite nodularity graphite morphology artificial intelligence machine learning

żeliwo sferoidalne guzkowatość grafitu morfologia grafitu sztuczna inteligencja uczenie maszynowe

Wydawca

AGH University of Science and Technology Press

Czasopismo

Journal of Casting & Materials Engineering

Rocznik

2021

Tom

Vol. 5, no. 4

Strony

94--102

Opis fizyczny

Bibliogr. 50 poz., fot., rys., wykr.

Twórcy

autor

Brait Maximilian

maximilian.brait@ogi.at

Austrian Foundry Research Institute, Montanuniversität Leoben/Chair of Casting Research, Montanuniversität Leoben, Franz-Josef-Straße 18, 8700 Leoben, Austria

autor

Koppensteiner Eduard

Austrian Foundry Research Institute, Parkstraße 21, 8700 Leoben, Austria

autor

Schindelbacher Gerhard

Austrian Foundry Research Institute, Parkstraße 21, 8700 Leoben, Austria

autor

Li Jiehua

Chair of Casting Research, Montanuniversität Leoben, Franz-Josef-Straße 18, 8700 Leoben, Austria

autor

Schumacher Peter

Austrian Foundry Research Institute, Parkstraße 21, 8700 Leoben, Austria/Chair of Casting Research, Montanuniversität Leoben, Franz-Josef-Straße 18, 8700 Leoben, Austria

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Uwagi

Opracowanie rekordu ze środków MNiSW, umowa Nr 461252 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2021)

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Bibliografia

Identyfikator YADDA

bwmeta1.element.baztech-77ac0da8-b00c-477e-b4bb-aef3c3c18e31