For Libraries For Publication Open Access Downloads About Us Contact Us
Paper Titles
Microstructural Aspects of the Zircaloy-4/SS-304L Interface Obtained by Diffusion Bonding Technique
p.17
Constitutive Modelling of an Industrial Rolled Sheet for a DIN 1623 St14 (DC04) Steel
p.31
Optimization of TIG Welding Process Parameters for X70-304L Dissimilar Joint Using Taguchi Method
p.51
Influence of Cellulosic Fluxes on the Chemical Composition, Microstructure, Inclusions and Micro-Hardness of SMAW Multi-Pass Welds of X42 Steel
p.62
Prediction of the Ultimate Tensile Strength in API X70 Line Pipe Steel Using an Artificial Neural Network Model
p.71
Electronic and Elastic Properties of TlX (X = N, P, As and Sb) in Zinc-Blende Structure
p.82
Anisotropic Behavior of Different Three-Dimensional Structures Materials under Thermal Stress Effect
p.95
Modelisation and Simulation of Cgs.op and Cgd.op Capacities of GaAs MESFETs OPFET
p.105
Structural and Anisotropic Elastic Properties of Hexagonal YMnO3 in Low Symmetry Determined by First-Principles Calculations
p.120

Prediction of the Ultimate Tensile Strength in API X70 Line Pipe Steel Using an Artificial Neural Network Model

Article Preview

Abstract:

An artificial neural network (ANN) model has been developed for the analysis and simulation of the correlation between the chemical composition and mechanical properties of high strength low alloy (HSLA) steel X70. The input parameters of the model consist of the base metal chemical composition (C, Si, Mn, the sum of Cr+Cu+Ni+Mo, the sum of Nb+Ti+V, carbon equivalent CEpcm) and the yield strength (YS). The outputs of the ANN model include the ultimate tensile strength (UTS) of the test material. Scatter plots, correlation coefficient (R) and mean relative error (MRE) were used to assess the performance of the developed neural network. Interestingly, the model output is efficient to calculate the mechanical properties of high strength low alloy steels, especially the ultimate tensile strength as a function of chemical composition and yield strength of the used material. The obtained results are in a good agreement with experimental ones, with high correlation coefficient and low mean relative error. The predictions accuracy of the developed model also conforms to the results of mean paired T-test.

You might also be interested in these eBooks
Industrial Processes of Multi-Material Assembly View Preview

Info:

Periodical:

Solid State Phenomena (Volume 297)

Pages:

71-81

DOI:

https://doi.org/10.4028/www.scientific.net/SSP.297.71

Citation:

Cite this paper

Online since:

September 2019

Authors:

Adel Saoudi*, Djahida Lerari, Farida Khamouli, L'Hadi Atoui, Khaldoun Bachari

Keywords:

ANN Model, Chemical Composition, HSLA, Ultimate Tensile Strength, Yield Strength

Export:

RIS, BibTeX

Price:

Permissions CCC:

Request Permissions

Permissions PLS:

Request Permissions

Сopyright:

© 2019 Trans Tech Publications Ltd. All Rights Reserved

Share:

Citation:

* - Corresponding Author

References

[1] Z. Zhang: Mater. China Vol. 35 (2016), 141.

Google Scholar

[2] M. Ayaz, D.M. Khaki, N.B.M. Arab, A. Noroozi: Int J Mater Res Vol. 104 (2013), 1212-1222.

Google Scholar

[3] S. Shanmugam, N. Ramisetti, R. Misra, J. Hartmann, S. Jansto: Mater. Sci. Eng., A Vol. 478 (2008), 26-37.

Google Scholar

[4] J. Zhao, Z. Jiang: Prog. Mater Sci. Vol. 94 (2018), 174-182.

Google Scholar

[5] M. Cabibbo, A. Fabrizi, M. Merlin, G. Garagnani: J. Mater. Sci. Vol. 43 (2008), 6857.

Google Scholar

[6] A. Nowotnik, T. Siwecki: J. Microsc. Vol. 237 (2010), 258-262.

Google Scholar

[7] H. Rahmanifard, T. Plaksina: Artif. Intell. Rev. Vol. (2018), 1-24.

Google Scholar

[8] P.N. Banu, S.D. Rani: Comput. Mater. Sci. Vol. 149 (2018), 259-266.

Google Scholar

[9] G. Liu, L. Jia, B. Kong, K. Guan, H. Zhang: Mater. Des. Vol. 129 (2017), 210-218.

Google Scholar

[10] J. Zhao, H. Ding, W. Zhao, M. Huang, D. Wei, Z. Jiang: Comput. Mater. Sci. Vol. 92 (2014), 47-56.

Google Scholar

[11] A. Jenab, I.S. Sarraf, D.E. Green, T. Rahmaan, M.J. Worswick: Mater. Des. Vol. 94 (2016), 262-273.

Google Scholar

[12] A. Powar, P. Date: Mater. Sci. Eng., A Vol. 628 (2015), 89-97.

Google Scholar

[13] S. Dey, N. Sultana, M.S. Kaiser, P. Dey, S. Datta: Mater. Des. Vol. 92 (2016), 522-534.

Google Scholar

[14] K. Guan, L. Jia, X. Chen, J. Weng, F. Ding, H. Zhang: Mater. Sci. Eng., A Vol. 605 (2014), 65-72.

Google Scholar

[15] G. Khalaj, T. Azimzadegan, M. Khoeini, M. Etaat: Neural Comput. Appl. Vol. 23 (2013), 2301-2308.

DOI: 10.1007/s00521-012-1182-0

Google Scholar

[16] T. Azimzadegan, M. Khoeini, M. Etaat, A. Khoshakhlagh: Neural Comput. Appl. Vol. 23 (2013), 1473-1480.

DOI: 10.1007/s00521-012-1097-9

Google Scholar

[17] J.W. D. M. Jones, K. J. Brown: Ironmaking Steelmaking Vol. 32 (2005), 435-442.

Google Scholar

[18] M.J. Faizabadi, G. Khalaj, H. Pouraliakbar, M.R. Jandaghi: Neural Comput. Appl. Vol. 25 (2014), 1993-1999.

Google Scholar

[19] H. Jafari, Z. Jafari: Journal of Bio-and Tribo-Corrosion Vol. 4 (2018), 24.

Google Scholar

[20] W. Liu, H. Pan, L. Li, H. Lv, Z. Wu, F. Cao, J. Zhu: J. Manuf. Process. Vol. 25 (2017), 418-425.

Google Scholar

[21] H. Pouraliakbar, M.-j. Khalaj, M. Nazerfakhari, G. Khalaj: J Iron Steel Res Int Vol. 22 (2015), 446-450.

DOI: 10.1016/s1006-706x(15)30025-x

Google Scholar

[22] R. Dimitriu, H. Bhadeshia, C. Fillon, C. Poloni: Mater. Manuf. Processes Vol. 24 (2008), 10-15.

Google Scholar

[23] H. Bhadeshia, R. Dimitriu, S. Forsik, J. Pak, J. Ryu: Mater. Sci. Technol. Vol. 25 (2009), 504-510.

Google Scholar

[24] Ş. Talaş: Mater. Des. Vol. 31 (2010), 2649-2653.

Google Scholar

[25] Specification API 5L. Specification for line pipe, 44th Edition ed., American Petroleum Institute, (2007).

Google Scholar

[26] K. Gurney, An introduction to neural networks, CRC press, (2014).

Google Scholar

[27] S. Soft: Tulsa, OK: Stat Soft Inc Vol. (2013).

Google Scholar

[28] A. Nazari: Neural Comput. Appl. Vol. 22 (2013), 731-745.

Google Scholar

[29] B. Show, R. Veerababu, R. Balamuralikrishnan, G. Malakondaiah: Mater. Sci. Eng., A Vol. 527 (2010), 1595-1604.

Google Scholar

[30] M.S. Mohebbi, M. Rezayat, M.H. Parsa, Š. Nagy, M. Nosko: Mater. Sci. Eng., A Vol. 723 (2018), 194-203.

Google Scholar

[31] Y. Zou, Y. Xu, Z. Hu, X. Gu, F. Peng, X. Tan, S. Chen, D. Han, R. Misra, G. Wang: Mater. Sci. Eng., A Vol. 675 (2016), 153-163.

Google Scholar

[32] P. Gong, E. Palmiere, W. Rainforth: Acta Mater. Vol. 97 (2015), 392-403.

Google Scholar

[33] Z. Dai, R. Ding, Z. Yang, C. Zhang, H. Chen: Acta Mater. Vol. 152 (2018), 288-299.

Google Scholar

[34] Y. Shao, C. Liu, Z. Yan, H. Li, Y. Liu: J. Mater. Sci. Technol. Vol. 34 (2018), 737-744.

Google Scholar

[35] D.C. Montgomery, G.C. Runger, Applied statistics and probability for engineers, John Wiley & Sons, (2010).

Google Scholar

[36] Minitab, MINITAB release 17: Statistical software for windows. Minitab Inc, USA, (2014).

Google Scholar

© 2025 Trans Tech Publications Ltd. All rights are reserved, including those for text and data mining, AI training, and similar technologies. For open access content, terms of the Creative Commons licensing CC-BY are applied.
Scientific.Net is a registered trademark of Trans Tech Publications Ltd.