<abstract xmlns="http://www.w3.org/1999/xhtml">

<p>Knowing the material properties is of a crucial importance when planning to manufacture some structure. 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 products 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 properties 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 comparing 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 between the two sets of values was below 5% and, in some cases, even negligible.</p>
</abstract>

Knowing the material properties is of a crucial importance when planning to manufacture some structure. 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 products 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 properties 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 comparing 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 between the two sets of values was below 5% and, in some cases, even negligible.

Predicting the mechanical properties of stainless steels using Artificial Neural Networks

Production Engineering Archives

This work is licensed under the Creative Commons Attribution 4.0 International License.

Knowing the material properties is of a crucial importance when planning to manufacture some structure. That is true for the steel structures, as well. Thus,...

Predicting the mechanical properties of stainless steels using Artificial Neural Networks

Online veröffentlicht: 26. Mai 2024

Seitenbereich: 225 - 232

Eingereicht: 29. Feb. 2024

Akzeptiert: 15. Apr. 2024

DOI: https://doi.org/10.30657/pea.2024.30.21

Schlüsselwörter
Stainless steel, Yield stress, Tensile strength, Hardness, ANN

© 2024 Djordje Ivković, published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Predicting the mechanical properties of stainless steels using Artificial Neural Networks

Online veröffentlicht: 26. Mai 2024

Seitenbereich: 225 - 232

Eingereicht: 29. Feb. 2024

Akzeptiert: 15. Apr. 2024

DOI: https://doi.org/10.30657/pea.2024.30.21

SchlüsselwörterStainless steel, Yield stress, Tensile strength, Hardness, ANN

© 2024 Djordje Ivković, published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Schlüsselwörter
Stainless steel, Yield stress, Tensile strength, Hardness, ANN