Modeling high-temperature mechanical properties of austenitic stainless steels by neural networks
[Display omitted] •Tensile properties of 18Cr-12Ni-Mo steels were modeled using neural networks.•Graphical user interface (GUI) was developed for easy use.•The model was able to correlate the relationships between input-output variables.•A virtual 18Cr-12Ni-Mo steels were created with mean values of...
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| Published in: | Computational materials science Vol. 179; p. 109617 |
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| Format: | Journal Article |
| Language: | English |
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15-06-2020
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| Abstract | [Display omitted]
•Tensile properties of 18Cr-12Ni-Mo steels were modeled using neural networks.•Graphical user interface (GUI) was developed for easy use.•The model was able to correlate the relationships between input-output variables.•A virtual 18Cr-12Ni-Mo steels were created with mean values of the databases.•Effect of temperature and composition on properties was estimated accurately.
An artificial neural network (ANN) model was designed to correlate the complex relations among composition, temperature, and mechanical properties of 18Cr-12Ni-Mo austenitic stainless steels. The developed model was used to estimate the composition-property and temperature-property correlations with 97% and 91% accuracy, for train and unseen test datasets. The ANN predictions are more accurate with experimental results as compared with the calculated properties of the existing model. The effective response of the alloying elements on the mechanical properties at ambient as well as elevated temperatures was quantitatively estimated with the help of the index of relative importance (IRI). The calculated results of the ANN model beneficial for both researchers as well as designers to guide actual experiments. Hence, this proposed technique will be helpful in developing the components of austenitic stainless steel with desired properties. |
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| AbstractList | [Display omitted]
•Tensile properties of 18Cr-12Ni-Mo steels were modeled using neural networks.•Graphical user interface (GUI) was developed for easy use.•The model was able to correlate the relationships between input-output variables.•A virtual 18Cr-12Ni-Mo steels were created with mean values of the databases.•Effect of temperature and composition on properties was estimated accurately.
An artificial neural network (ANN) model was designed to correlate the complex relations among composition, temperature, and mechanical properties of 18Cr-12Ni-Mo austenitic stainless steels. The developed model was used to estimate the composition-property and temperature-property correlations with 97% and 91% accuracy, for train and unseen test datasets. The ANN predictions are more accurate with experimental results as compared with the calculated properties of the existing model. The effective response of the alloying elements on the mechanical properties at ambient as well as elevated temperatures was quantitatively estimated with the help of the index of relative importance (IRI). The calculated results of the ANN model beneficial for both researchers as well as designers to guide actual experiments. Hence, this proposed technique will be helpful in developing the components of austenitic stainless steel with desired properties. |
| ArticleNumber | 109617 |
| Author | Yeom, Jong-Taek Park, Chan Hee Hong, Jae-Keun Reddy, N. S. Narayana, P.L. Maurya, A.K. Lee, Sang Won |
| Author_xml | – sequence: 1 givenname: P.L. surname: Narayana fullname: Narayana, P.L. organization: Advanced Metals Division, Korea Institute of Materials Science, Changwon 51508, Republic of Korea – sequence: 2 givenname: Sang Won surname: Lee fullname: Lee, Sang Won organization: Advanced Metals Division, Korea Institute of Materials Science, Changwon 51508, Republic of Korea – sequence: 3 givenname: Chan Hee surname: Park fullname: Park, Chan Hee organization: Advanced Metals Division, Korea Institute of Materials Science, Changwon 51508, Republic of Korea – sequence: 4 givenname: Jong-Taek surname: Yeom fullname: Yeom, Jong-Taek email: yjt96@kims.re.kr organization: Advanced Metals Division, Korea Institute of Materials Science, Changwon 51508, Republic of Korea – sequence: 5 givenname: Jae-Keun orcidid: 0000-0002-9841-8311 surname: Hong fullname: Hong, Jae-Keun organization: Advanced Metals Division, Korea Institute of Materials Science, Changwon 51508, Republic of Korea – sequence: 6 givenname: A.K. surname: Maurya fullname: Maurya, A.K. organization: Advanced Metals Division, Korea Institute of Materials Science, Changwon 51508, Republic of Korea – sequence: 7 givenname: N. S. orcidid: 0000-0003-4206-4515 surname: Reddy fullname: Reddy, N. S. email: nsreddy@gnu.ac.kr organization: School of Materials Science and Engineering, Gyeongsang National University, Jinju 52828, Republic of Korea |
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| Cites_doi | 10.2355/isijinternational.34.764 10.1016/j.matlet.2015.06.015 10.1016/j.proeng.2012.06.395 10.1016/j.jmrt.2015.04.001 10.1007/BF02644292 10.1007/978-3-642-35167-9_3 10.1016/j.commatsci.2015.01.031 10.1179/026708302225002065 10.1016/j.matdes.2007.02.009 10.1016/j.msea.2008.12.022 10.1016/j.jcsr.2009.12.016 10.1016/S1003-6326(13)62530-3 10.1016/j.matdes.2005.06.003 10.1016/j.actamat.2018.08.022 10.2355/isijinternational.39.966 10.1016/j.commatsci.2015.05.026 |
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| Keywords | Index of the relative importance Austenitic stainless steels Artificial neural networks Graphical user interface Property prediction |
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| References | Priddy, Keller (b0120) 2005 Bandyopadhyay, Kameda, McMahon (b0160) 1983; 14 Saravanakumar, Jothimani, Sureshbabu, Ayyappan, Noorullah, Venkatakrishnan (b0060) 2012; 38 Sha, Malinov (b0100) 2009 J. Horak, V. Sikka, D. Raske, Review of mechanical properties and microstructures of Types 304 and 316 stainless steel after long-term aging, Oak Ridge National Lab., TN (USA); Argonne National Lab., IL (USA), 1983. Wikipedia contributors, Prices of chemical elements, Wikipedia, The Free Encyclopedia, 2018 (accessed 21 November 2018 09:18 UTC.2018). Sourmail, Bhadeshia, MacKay (b0075) 2002; 18 [accessed 2017-09-24]. Davis (b0005) 1997 Moisă (b0145) 2011 Sha, Edwards (b0095) 2007; 28 Creep Database Ohkubo, Miyakusu, Uematsu, Kimura (b0140) 1994; 34 N.S. Reddy, K. J, S.-G. Hong, J.S. Lee, Modeling medium carbon steels by using artificial neural networks, 2009. Gardner, Insausti, Ng, Ashraf (b0010) 2010; 66 Samantaray, Kumar, Bhaduri, Dutta (b0020) 2013; 2 G. Callis, New competitive realities in steel, Millennium Steel (2006) 18. Yang, Zhu, Nong, He, Lai, Liu, Liu (b0055) 2013; 23 Reddy, Krishnaiah, Young, Lee (b0045) 2015; 101 A. Grimmond, Energy Subsidies in the Steel Industry, OECD Steel Committe Yetim, Codur, Yazici (b0070) 2015; 158 (2011). Cassar, de Carvalho, Zanotto (b0050) 2018; 159 Desu, Krishnamurthy, Balu, Gupta, Singh (b0080) 2016; 5 C. Blundell, J. Cornebise, K. Kavukcuoglu, D. Wierstra, Weight uncertainty in neural networks, arXiv preprint arXiv:1505.05424 (2015). Hkdh (b0090) 1999; 39 Reddy (b0135) 2004 Go, Lee (b0125) 1999 Reddy, Panigrahi, Ho, Kim, Lee (b0110) 2015; 107 Sudhakar, Haque (b0065) 2013 Creese, Adithan (b0025) 1992 Okuyucu, Kurt, Arcaklioglu (b0105) 2007; 28 Y. Gorash, H. Altenbach, G. Lvov, Modelling of high-temperature inelastic behavior of the austenitic steel AISI type 316 using a continuum damage mechanics approach, 2012. Bhadeshia, Honeycombe (b0155) 2017 Sha (10.1016/j.commatsci.2020.109617_b0100) 2009 Reddy (10.1016/j.commatsci.2020.109617_b0110) 2015; 107 Hkdh (10.1016/j.commatsci.2020.109617_b0090) 1999; 39 Yetim (10.1016/j.commatsci.2020.109617_b0070) 2015; 158 Desu (10.1016/j.commatsci.2020.109617_b0080) 2016; 5 Priddy (10.1016/j.commatsci.2020.109617_b0120) 2005 Davis (10.1016/j.commatsci.2020.109617_b0005) 1997 10.1016/j.commatsci.2020.109617_b0040 10.1016/j.commatsci.2020.109617_b0030 Creese (10.1016/j.commatsci.2020.109617_b0025) 1992 10.1016/j.commatsci.2020.109617_b0130 Saravanakumar (10.1016/j.commatsci.2020.109617_b0060) 2012; 38 Gardner (10.1016/j.commatsci.2020.109617_b0010) 2010; 66 Reddy (10.1016/j.commatsci.2020.109617_b0045) 2015; 101 10.1016/j.commatsci.2020.109617_b0035 10.1016/j.commatsci.2020.109617_b0015 10.1016/j.commatsci.2020.109617_b0115 Ohkubo (10.1016/j.commatsci.2020.109617_b0140) 1994; 34 Yang (10.1016/j.commatsci.2020.109617_b0055) 2013; 23 Reddy (10.1016/j.commatsci.2020.109617_b0135) 2004 Samantaray (10.1016/j.commatsci.2020.109617_b0020) 2013; 2 Sudhakar (10.1016/j.commatsci.2020.109617_b0065) 2013 Sourmail (10.1016/j.commatsci.2020.109617_b0075) 2002; 18 Sha (10.1016/j.commatsci.2020.109617_b0095) 2007; 28 Moisă (10.1016/j.commatsci.2020.109617_b0145) 2011 Bhadeshia (10.1016/j.commatsci.2020.109617_b0155) 2017 10.1016/j.commatsci.2020.109617_b0150 10.1016/j.commatsci.2020.109617_b0085 Go (10.1016/j.commatsci.2020.109617_b0125) 1999 Bandyopadhyay (10.1016/j.commatsci.2020.109617_b0160) 1983; 14 Okuyucu (10.1016/j.commatsci.2020.109617_b0105) 2007; 28 Cassar (10.1016/j.commatsci.2020.109617_b0050) 2018; 159 |
| References_xml | – year: 1997 ident: b0005 article-title: ASM Specialty Handbook: Heat-resistant Materials contributor: fullname: Davis – start-page: 95 year: 2011 end-page: 100 ident: b0145 article-title: Influence of chemical composition on stainless steels mechanical properties publication-title: Proceedings of the 13th WSEAS International Conference on Mathematical and Computational Methods in Science and Engineering, World Scientific and Engineering Academy and Society (WSEAS) contributor: fullname: Moisă – volume: 34 start-page: 764 year: 1994 end-page: 772 ident: b0140 article-title: Effect of alloying elements on the mechanical properties of the stable austenitic stainless steel publication-title: ISIJ Int. contributor: fullname: Kimura – volume: 28 start-page: 1747 year: 2007 end-page: 1752 ident: b0095 article-title: The use of artificial neural networks in materials science based research publication-title: Mater. Des. contributor: fullname: Edwards – volume: 101 start-page: 120 year: 2015 end-page: 126 ident: b0045 article-title: Design of medium carbon steels by computational intelligence techniques publication-title: Comput. Mater. Sci. contributor: fullname: Lee – volume: 18 start-page: 655 year: 2002 end-page: 663 ident: b0075 article-title: Neural network model of creep strength of austenitic stainless steels publication-title: Mater. Sci. Technol. contributor: fullname: MacKay – volume: 158 start-page: 170 year: 2015 end-page: 173 ident: b0070 article-title: Using of artificial neural network for the prediction of tribological properties of plasma nitrided 316L stainless steel publication-title: Mater. Lett. contributor: fullname: Yazici – year: 2004 ident: b0135 article-title: Study of Some Complex Metallurgical Systems by Computational Intelligence Techniques contributor: fullname: Reddy – volume: 159 start-page: 249 year: 2018 end-page: 256 ident: b0050 article-title: Predicting glass transition temperatures using neural networks publication-title: Acta Mater. contributor: fullname: Zanotto – start-page: 1154 year: 1999 end-page: 1157 ident: b0125 article-title: Analyzing weight distribution of neural networks, Neural Networks, 1999 publication-title: IJCNN'99. International Joint Conference on, IEEE contributor: fullname: Lee – year: 2005 ident: b0120 article-title: Artificial Neural Networks: An Introduction contributor: fullname: Keller – volume: 66 start-page: 634 year: 2010 end-page: 647 ident: b0010 article-title: Elevated temperature material properties of stainless steel alloys publication-title: J. Constr. Steel Res. contributor: fullname: Ashraf – volume: 2 start-page: 149 year: 2013 end-page: 153 ident: b0020 article-title: Microstructural evolution and mechanical properties of type 304 L stainless steel processed in semi-solid state publication-title: Int. J. Metallurgical Eng. contributor: fullname: Dutta – volume: 38 start-page: 3418 year: 2012 end-page: 3425 ident: b0060 article-title: Prediction of mechanical properties of low carbon steel in hot rolling process using artificial neural network model publication-title: Procedia Eng. contributor: fullname: Venkatakrishnan – volume: 107 start-page: 175 year: 2015 end-page: 183 ident: b0110 article-title: Artificial neural network modeling on the relative importance of alloying elements and heat treatment temperature to the stability of α and β phase in titanium alloys publication-title: Comput. Mater. Sci. contributor: fullname: Lee – start-page: 40 year: 2013 end-page: 44 ident: b0065 article-title: Artificial Neural Network Model: Prediction of Mechanical Properties in Beta-titanium Biomaterial, Applied Mechanics and Materials contributor: fullname: Haque – year: 1992 ident: b0025 article-title: Estimating and Costing for the Metal Manufacturing Industries contributor: fullname: Adithan – year: 2017 ident: b0155 article-title: Steels: Microstructure and Properties contributor: fullname: Honeycombe – volume: 39 start-page: 966 year: 1999 end-page: 979 ident: b0090 article-title: Neural networks in materials science publication-title: ISIJ Int. contributor: fullname: Hkdh – volume: 14 start-page: 881 year: 1983 end-page: 888 ident: b0160 article-title: Hydrogen-induced cracking in 4340-type steel: effects of composition, yield strength, and H2 pressure publication-title: Metall. Trans. A contributor: fullname: McMahon – volume: 5 start-page: 13 year: 2016 end-page: 20 ident: b0080 article-title: Mechanical properties of Austenitic Stainless Steel 304L and 316L at elevated temperatures publication-title: J. Mater. Res. Technol. contributor: fullname: Singh – volume: 23 start-page: 788 year: 2013 end-page: 795 ident: b0055 article-title: Prediction of mechanical properties of A357 alloy using artificial neural network publication-title: Trans. Nonferrous Metals Soc. China contributor: fullname: Liu – year: 2009 ident: b0100 article-title: Titanium Alloys: Modelling of Microstructure, Properties and Applications contributor: fullname: Malinov – volume: 28 start-page: 78 year: 2007 end-page: 84 ident: b0105 article-title: Artificial neural network application to the friction stir welding of aluminum plates publication-title: Mater. Des. contributor: fullname: Arcaklioglu – year: 1997 ident: 10.1016/j.commatsci.2020.109617_b0005 contributor: fullname: Davis – year: 1992 ident: 10.1016/j.commatsci.2020.109617_b0025 contributor: fullname: Creese – year: 2004 ident: 10.1016/j.commatsci.2020.109617_b0135 contributor: fullname: Reddy – volume: 34 start-page: 764 issue: 9 year: 1994 ident: 10.1016/j.commatsci.2020.109617_b0140 article-title: Effect of alloying elements on the mechanical properties of the stable austenitic stainless steel publication-title: ISIJ Int. doi: 10.2355/isijinternational.34.764 contributor: fullname: Ohkubo – volume: 2 start-page: 149 issue: 2 year: 2013 ident: 10.1016/j.commatsci.2020.109617_b0020 article-title: Microstructural evolution and mechanical properties of type 304 L stainless steel processed in semi-solid state publication-title: Int. J. Metallurgical Eng. contributor: fullname: Samantaray – ident: 10.1016/j.commatsci.2020.109617_b0030 – ident: 10.1016/j.commatsci.2020.109617_b0085 – year: 2005 ident: 10.1016/j.commatsci.2020.109617_b0120 contributor: fullname: Priddy – start-page: 95 year: 2011 ident: 10.1016/j.commatsci.2020.109617_b0145 article-title: Influence of chemical composition on stainless steels mechanical properties contributor: fullname: Moisă – year: 2017 ident: 10.1016/j.commatsci.2020.109617_b0155 contributor: fullname: Bhadeshia – start-page: 40 year: 2013 ident: 10.1016/j.commatsci.2020.109617_b0065 contributor: fullname: Sudhakar – volume: 158 start-page: 170 year: 2015 ident: 10.1016/j.commatsci.2020.109617_b0070 article-title: Using of artificial neural network for the prediction of tribological properties of plasma nitrided 316L stainless steel publication-title: Mater. Lett. doi: 10.1016/j.matlet.2015.06.015 contributor: fullname: Yetim – ident: 10.1016/j.commatsci.2020.109617_b0150 – year: 2009 ident: 10.1016/j.commatsci.2020.109617_b0100 contributor: fullname: Sha – volume: 38 start-page: 3418 year: 2012 ident: 10.1016/j.commatsci.2020.109617_b0060 article-title: Prediction of mechanical properties of low carbon steel in hot rolling process using artificial neural network model publication-title: Procedia Eng. doi: 10.1016/j.proeng.2012.06.395 contributor: fullname: Saravanakumar – ident: 10.1016/j.commatsci.2020.109617_b0130 – volume: 5 start-page: 13 issue: 1 year: 2016 ident: 10.1016/j.commatsci.2020.109617_b0080 article-title: Mechanical properties of Austenitic Stainless Steel 304L and 316L at elevated temperatures publication-title: J. Mater. Res. Technol. doi: 10.1016/j.jmrt.2015.04.001 contributor: fullname: Desu – volume: 14 start-page: 881 issue: 4 year: 1983 ident: 10.1016/j.commatsci.2020.109617_b0160 article-title: Hydrogen-induced cracking in 4340-type steel: effects of composition, yield strength, and H2 pressure publication-title: Metall. Trans. A doi: 10.1007/BF02644292 contributor: fullname: Bandyopadhyay – ident: 10.1016/j.commatsci.2020.109617_b0015 doi: 10.1007/978-3-642-35167-9_3 – volume: 101 start-page: 120 year: 2015 ident: 10.1016/j.commatsci.2020.109617_b0045 article-title: Design of medium carbon steels by computational intelligence techniques publication-title: Comput. Mater. Sci. doi: 10.1016/j.commatsci.2015.01.031 contributor: fullname: Reddy – volume: 18 start-page: 655 issue: 6 year: 2002 ident: 10.1016/j.commatsci.2020.109617_b0075 article-title: Neural network model of creep strength of austenitic stainless steels publication-title: Mater. Sci. Technol. doi: 10.1179/026708302225002065 contributor: fullname: Sourmail – volume: 28 start-page: 1747 issue: 6 year: 2007 ident: 10.1016/j.commatsci.2020.109617_b0095 article-title: The use of artificial neural networks in materials science based research publication-title: Mater. Des. doi: 10.1016/j.matdes.2007.02.009 contributor: fullname: Sha – ident: 10.1016/j.commatsci.2020.109617_b0035 – ident: 10.1016/j.commatsci.2020.109617_b0115 doi: 10.1016/j.msea.2008.12.022 – volume: 66 start-page: 634 issue: 5 year: 2010 ident: 10.1016/j.commatsci.2020.109617_b0010 article-title: Elevated temperature material properties of stainless steel alloys publication-title: J. Constr. Steel Res. doi: 10.1016/j.jcsr.2009.12.016 contributor: fullname: Gardner – volume: 23 start-page: 788 issue: 3 year: 2013 ident: 10.1016/j.commatsci.2020.109617_b0055 article-title: Prediction of mechanical properties of A357 alloy using artificial neural network publication-title: Trans. Nonferrous Metals Soc. China doi: 10.1016/S1003-6326(13)62530-3 contributor: fullname: Yang – volume: 28 start-page: 78 issue: 1 year: 2007 ident: 10.1016/j.commatsci.2020.109617_b0105 article-title: Artificial neural network application to the friction stir welding of aluminum plates publication-title: Mater. Des. doi: 10.1016/j.matdes.2005.06.003 contributor: fullname: Okuyucu – volume: 159 start-page: 249 year: 2018 ident: 10.1016/j.commatsci.2020.109617_b0050 article-title: Predicting glass transition temperatures using neural networks publication-title: Acta Mater. doi: 10.1016/j.actamat.2018.08.022 contributor: fullname: Cassar – start-page: 1154 year: 1999 ident: 10.1016/j.commatsci.2020.109617_b0125 article-title: Analyzing weight distribution of neural networks, Neural Networks, 1999 contributor: fullname: Go – volume: 39 start-page: 966 issue: 10 year: 1999 ident: 10.1016/j.commatsci.2020.109617_b0090 article-title: Neural networks in materials science publication-title: ISIJ Int. doi: 10.2355/isijinternational.39.966 contributor: fullname: Hkdh – volume: 107 start-page: 175 year: 2015 ident: 10.1016/j.commatsci.2020.109617_b0110 article-title: Artificial neural network modeling on the relative importance of alloying elements and heat treatment temperature to the stability of α and β phase in titanium alloys publication-title: Comput. Mater. Sci. doi: 10.1016/j.commatsci.2015.05.026 contributor: fullname: Reddy – ident: 10.1016/j.commatsci.2020.109617_b0040 |
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•Tensile properties of 18Cr-12Ni-Mo steels were modeled using neural networks.•Graphical user interface (GUI) was developed for easy use.•The... |
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| Title | Modeling high-temperature mechanical properties of austenitic stainless steels by neural networks |
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