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

Seung-Hyeok Shin Sang-Gyu Kim Byoungchul Hwang
Archives of Metallurgy and Materials | 2021 | vol. 66 | No 3 | 719-723 | DOI: 10.24425/amm.2021.136368
Keywords Artificial Neural Network (ANN) dual-phase steels alloying element microstructural factor tensile properties
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Abstract

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.
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Bibliography

[1] H.L. Kim, S.H. Bang, J.M. Choi, N.H. Tak, S.W. Lee, S.H. Park, Met. Mater. Int. 26, 1757-1765 (2020).
[2] S.I. Lee, J. Lee, B. Hwang, Mater. Sci. Eng. A 758, 56-59 (2019).
[3] S.I. Lee, S.Y. Lee, J. Han, B. Hwang, Mater. Sci. Eng. A 742, 334-343 (2019).
[4] S.I. Lee, S.Y. Lee, S.G. Lee, H.G. Jung, B. Hwang, Met. Mater. Int. 24, 1221-1231 (2018).
[5] S.Y. Lee, S.I. Lee, B. Hwang, Mater. Sci. Eng. A 711, 22-28 (2018).
[6] W . Bleck, S. Papaefthymiou, A. Frehn, Steel Res. Int. 75, 704-710 (2004).
[7] M .J Jang, H. Kwak, Y.W Lee, Y.J. Jeong, J. Choi, Y.H. Jo, W.M. Choi, H.J. Sung, E.Y. Yoon, S. Praveen, S. Lee, B.J. Lee, M.I. Abd El Aal, H.S. Kim, Met. Mater. Int. 25, 277-284 (2019).
[8] N. Saeidi, M. Jafari, J.G. Kim, F. Ashrafizadeh, H.S. Kim, Met. Mater. Int. 26, 168-178 (2020).
[9] M . Soleimani, H. Mirzadeh, C. Dehghanian, Met. Mater. Int. 26, 882-890 (2020).
[10] C.C. Tasan, M. Diehl, D. Yan, M. Bechtold, F. Roters, L. Schemmann, C. Zheng, N. Peranio, D. Ponge, M. Koyama, K. Tsuzaki, D. Raabe, Annual Rev. Mater. Res. 45, 391-431 (2015).
[11] D. Das, P.P. Chattopadhyay, J. Mater. Sci. 44, 2957-2965 (2009).
[12] D.K. Mondal, R.M. Dey, Mater. Sci. Eng. A 149, 173-181 (1992).
[13] M . Sarwar, R. Priestner, J. Mater. Sci. 31, 2091-2095 (1996).
[14] B. Hwang, T. Cao, S.Y. Shin, S. Lee, S.J. Kim, Mater. Sci. Tech. 21, 967-975 (2005).
[15] F. Najafkhani, H. Mirzadeh, M. Zamani, Met. Mater. Int. 25, 1039-1046 (2019).
[16] J.I. Yoon, J. Jung, H.H. Lee, J.Y. Kim, H.S. Kim, Met. Mater. Int. 25, 1161-1169 (2019).
[17] H. Duan, Y. Li, G. He, J. Zhang, Int. J. Mod. Phys. B 23, 1191- 1196 (2009).
[18] S. Krajewski, J. Nowacki, Arch. Civ. Mech. Eng. 14, 278-286 (2014).
[19] N.S. Reddy, C.H. Park, Y.H. Lee, C.S. Lee, Mater. Sci. Tech. 24, 294-301 (2008).
[20] N.S. Reddy, Y.H. Lee, C.H. Park, C.S. Lee, Mater. Sci. Eng. A 492, 276-282 (2008).
[21] N.S. Reddy, B.B. Panigrahi, M.H. Choi, J.H. Kim, C.S. Lee, Comput. Mater. Sci. 107, 175-183 (2015).
[22] N.S. Reddy, J. Krishnaiah, S.G. Hong, J.S. Lee, Mater. Sci. Eng. A 508, 93-105 (2009).
[23] T. Dutta, S. Dey, S. Datta, D. Das, Comput. Mater. Sci. 157, 6-16 (2019).
[24] C. Lin, P.L. Nrayana, N.S. Reddy, S.W. Choi, J.T. Yeom, J.K Hong, C.H. Park, J. Mater. Sci. Tech. 35, 907-916 (2019).
[25] I .D. Jung, D.S. Shin, D. Kim, J. Lee, M.S. Lee, H.J. Son, N.S. Reddy, M. Kim, S.K. Moon, K.T. Kim, J. Yu, S. Kim, S.J. Park, H. Sung, Materialia 11, 100699 (2020).
[26] H.S. Lim, J.Y. Kim, B. Hwang, J. Korean. Soc. Heat Treat. 30, 106-112 (2017).
[27] S. Sodjit, V. Uthaisangsuk, Mater. Des. 41, 370-379 (2012).
[28] Z. Jiang, Z. Guan, J. Lian, Mater. Sci. Eng. A 190, 55-64 (1995).
[29] P . Chang, A.G. Preban, Acta Metall. 33, 897-903 (1985).
[30] N.D. Beynon, S. Oliver, T.B. Jones, G. Fourlaris, Mater. Sci. Tech, 21, 771-778 (2005).
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Authors and Affiliations

Seung-Hyeok Shin
1
ORCID: ORCID
Sang-Gyu Kim
1
ORCID: ORCID
Byoungchul Hwang
1
ORCID: ORCID
  1. Seoul National University of Science and Technology, Department of Materials Science and Engineering, Seoul, 01811, Republic of Korea

Effect of Pre-strain on Hydrogen Embrittlement in Intercritically Annealed Fe-6.5Mn-0.08C Medium-Mn steels

Sang-Gyu Kim Young-Chul Yoon Seok-Woo Ko Byoungchul Hwang
Archives of Metallurgy and Materials | 2022 | vol. 67 | No 4 | 1491-1495 | DOI: 10.24425/amm.2022.141080
Keywords medium-manganese steel hydrogen embrittlement electrochemical charging Pre-strain Slow strain-rate tensile (SSRT) test
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Abstract

The present research deals with the effect of pre-strain on the hydrogen embrittlement behavior of intercritically annealed medium-Mn steels. A slow strain-rate tensile test was conducted after hydrogen charging by an electrochemical permeation method. Based on EBSD and XRD analysis results, the microstructure was composed of martensite and retained austenite of which fraction increased with an increase in the intercritical annealing temperature. The tensile test results showed that the steel with a higher fraction of retained austenite had relatively high hydrogen embrittlement resistance because the retained austenite acts as an irreversible hydrogen trap site. As the amount of pre-strain was increased, the hydrogen embrittlement resistance decreased notably due to an increase in the dislocation density and strain-induced martensite transformation.
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Authors and Affiliations

Sang-Gyu Kim
1
ORCID: ORCID
Young-Chul Yoon
1
ORCID: ORCID
Seok-Woo Ko
1
ORCID: ORCID
Byoungchul Hwang
1
ORCID: ORCID
  1. Seoul National University of Science and Technology, Department of Materials Science and Engineering, Seoul, 01811, Republic of Korea

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