Predicting the mechanical properties of stainless steels using Artificial Neural Networks

• Autorzy: Ivković Djordje , Arsić Dušan , Adamović Dragan , Nikolić Ružica , Mitrović Andjela , Otakar Bokuvka

Identyfikatory

DOI

10.30657/pea.2024.30.21

• Języki publikacji: EN

Abstrakty

EN

Knowing the material properties is of a crucial importance when planning to manufacture some struc-ture. That is true for the steel structures, as well. Thus, for the proper planning of a certain steel part or a structure production, one must be aware of the properties of the material, to be able to make a qualified decision, which material should be used. Considering that the manufacturing of steel prod-ucts is constantly growing in various branches of industry and engineering, the problem of predicting the material properties, needed to satisfy the requirements for the certain part efficient and reliable functioning, becomes an imperative in the design process. A method of predicting four material prop-erties of the two stainless steels, by use of the artificial neural network (ANN) is presented in this article. Those properties were predicted based on the particular steels’ known chemical compositions and the corresponding material properties available in the Cambridge Educational System EDU PACK 2010 software, using neural network module of MathWorks Matlab. The method was verified by com-paring the values of the material properties predicted by this method to known values of properties for the two stainless steels, X5CrNi18-10 (AISI 304), X5CrNiMo17-12-2 (AISI 316). The difference be-tween the two sets of values was below 5% and, in some cases, even negligible.

Słowa kluczowe

EN

Stainless steel; Yield stress; Tensile strength; Hardness; ANN

PL

stal nierdzewna; plonowanie; wytrzymałość na rozciąganie; twardość; ANN

• Wydawca: Oficyna Wydawnicza Stowarzyszenia Menedżerów Jakości i Produkcji
• Czasopismo: Production Engineering Archives
• Rocznik: 2024
• Tom: Vol. 30, Iss. 2
• Strony: 225--232
• Opis fizyczny: Bibliogr. 30 poz., rys., tab.

Twórcy

autor

Ivković Djordje

• Faculty of Engineering, University of Kragujevac, Sestre Janjić 6, 34000 Kragujevac, Serbia

• https://orcid.org/0000-0002-5747-7876

autor

Arsić Dušan

• Faculty of Engineering, University of Kragujevac, Sestre Janjić 6, 34000 Kragujevac, Serbia

• https://orcid.org/0000-0003-0326-0898

autor

Adamović Dragan

• Faculty of Engineering, University of Kragujevac, Sestre Janjić 6, 34000 Kragujevac, Serbia

• https://orcid.org/0000-0003-3459-3011

autor

Nikolić Ružica

• Research Centre, University of Žilina, Univerzitna 8215/1, 010 26 Žilina, Slovakia

• https://orcid.org/0000-0003-3042-8916

autor

Mitrović Andjela

• Faculty of Engineering, University of Kragujevac, Sestre Janjić 6, 34000 Kragujevac, Serbia

• https://orcid.org/0009-0008-5300-5653

autor

Otakar Bokuvka

• Faculty of Mechanical Engineering, University of Žilina, Univerzitna 8215/1, 010 26 Žilina, Slovakia

• https://orcid.org/0000-0001-9928-2332

Bibliografia

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Uwagi

Opracowanie rekordu ze środków MNiSW, umowa nr SONP/SP/546092/2022 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2024).