Application of Artificial Neural Network to Predict the Tensile Properties of Dual-Phase Steels

• Autorzy: Shin Seung-Hyeok , Kim Sang-Gyu , Hwang Byoungchul

Identyfikatory

DOI

10.24425/amm.2021.136368

• Języki publikacji: EN

Abstrakty

EN

An artificial neural network (ANN) model was developed to predict the tensile properties of dual-phase steels in terms of alloying elements and microstructural factors. The developed ANN model was confirmed to be more reasonable than the multiple linear regression model to predict the tensile properties. In addition, the 3D contour maps and an average index of the relative importance calculated by the developed ANN model, demonstrated the importance of controlling microstructural factors to achieve the required tensile properties of the dual-phase steels. The ANN model is expected to be useful in understanding the complex relationship between alloying elements, microstructural factors, and tensile properties in dual-phase steels.

Słowa kluczowe

EN

artificial neural network; ANN; dual-phase steels; alloying element; microstructural factor; tensile properties

• Wydawca: Institute of Metallurgy and Materials Science of Polish Academy of Sciences ; Committee of Materials Engineering and Metallurgy of Polish Academy of Sciences
• Czasopismo: Archives of Metallurgy and Materials
• Rocznik: 2021
• Tom: Vol. 66, iss. 3
• Strony: 719--723
• Opis fizyczny: Bibliogr. 30 poz., rys., tab., wykr., wzory

Twórcy

autor

Shin Seung-Hyeok

• Seoul National University of Science and Technology, Department of Materials Science and Engineering, Seoul, 01811, Republic of Korea

• https://orcid.org/0000-0002-0458-5806

autor

Kim Sang-Gyu

• Seoul National University of Science and Technology, Department of Materials Science and Engineering, Seoul, 01811, Republic of Korea

• https://orcid.org/0000-0003-2237-8517

autor

Hwang Byoungchul

• Seoul National University of Science and Technology, Department of Materials Science and Engineering, Seoul, 01811, Republic of Korea

• https://orcid.org/0000-0001-6330-4747

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Uwagi

1. This study was supported by the Technology Innovation Program (Grant No. 10063488) funded by the Ministry of Trade, Industry and Energy (MOTIE), South Korea, and the Basic Science Research Program, National Research Foundation of Korea (NRF-2017R1A2B2009336)

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