Artificial Neural Network to Predict the Hot Deformation Behavior of Super 13Cr Martensitic Stainless Steel

Ying Han; Guan Jun Qiao; Dong Na Yan; De Ning Zou

doi:10.4028/www.scientific.net/MSF.695.361

Paper Titles

Research on the Optimization of Ni/Si Atomic Ratio for Cu-Ni-Si Alloys with High Strength and High Electrical Conductivity
p.344

Oxidation Behavior of As-Forged Fe-25wt%Cr Alloys at 1100°C in Air
p.348

Hydrogen Variation in Molten Steel during LF Refining
p.353

Microstructure and Magnetic Properties of Undercooled Fe-50at%Ni Alloys
p.357

Artificial Neural Network to Predict the Hot Deformation Behavior of Super 13Cr Martensitic Stainless Steel
p.361

Oxidation Behaviors of Pure Ti Thermal Plasma Spray Coated Mo-Si-B Alloys
p.365

Esterification of Chemical Functional Single-Wall Carbon Nanotubes
p.373

Development of Carbon Nanotube - Reinforced Silk and Cannabis Fibers by an Electrophoretic Deposition Method
p.377

Constitutive Equation Development for Large Strain Rate Deformation Processing of 2205 Duplex Stainless Steels
p.381

HomeMaterials Science ForumMaterials Science Forum Vol. 695Artificial Neural Network to Predict the Hot...

Artificial Neural Network to Predict the Hot Deformation Behavior of Super 13Cr Martensitic Stainless Steel

Abstract:

The hot deformation behavior of super 13Cr martensitic stainless steel was investigated using artificial neural network (ANN). Hot compression tests were carried out at the temperature range of 950°C to 1200°C and strain rate range of 0.1–50s^–1 at an interval of an order of magnitude. Based on the limited experimental data, the ANN model for the constitutive relationship existed between flow stress and strain, strain rate and deformation temperature was developed by back-propagation (BP) neural network method. A three layer structured network with one hidden layer and ten hidden neurons was trained and the normalization method was employed in training for avoiding over fitting. Modeling results show that the developed ANN model can efficiently predict the flow stress of the steel and reflect the hot deformation behavior in the whole deforming process.

You might also be interested in these eBooks

View Preview

Info:

Periodical:

Materials Science Forum (Volume 695)

Pages:

361-364

DOI:

https://doi.org/10.4028/www.scientific.net/MSF.695.361

Citation:

Cite this paper

Online since:

July 2011

Authors:

Ying Han, Guan Jun Qiao, Dong Na Yan, De Ning Zou

Keywords:

Artificial Neural Network (ANN), BP Neural Network (BPNN), Hot Deformation, Martensitic Stainless Steel

Export:

RIS, BibTeX

Price:

Permissions:

Request Permissions

References

[1] H. Nakamichi, K. Sato, Y. Miyata, et al: Corros Sci. Vol. 50 (2008), p.309.

Google Scholar

[2] M. Ueda, H. Amaya, K. Ogawa, et al: Corrosion Resistance of Weldable Super 13Cr Stainless Steel in H2S Containing CO2 Environments [C]/NACE. Corrosion 96. Denver: NACE International, 1996: 58.

Google Scholar

[3] A. Griffiths, W. Nimmo, B. Roebuck, et al: Mater Sci Eng. Vol. 384A (2004), p.83.

Google Scholar

[4] D. Carrouge, H. K. D. H. Bhadeshia, P. Woollin: Sci Technol Weld Joi. Vol. 9 (2004), p.377.

Google Scholar

[5] Sumantra Mandal, P.V. Sivaprasad, S. Venugopal, K.P.N. Murthy: Appl Soft Comput. Vol. 9 (2009), p.237.

Google Scholar

[6] P.S. Robi, U.S. Dixit: J. Mater. Proce. Tech. Vol. 142 (2003), p.289.

Google Scholar

[7] G. Ganesan, K. Raghukandan, R. Karthikeyan, et al: J Mater Process Tech. Vol. 166 (2005), p.423.

Google Scholar

[8] J.H. Yu, D.N. Zou, Y. Han, et al: Mater. Sci. Forum. Vol. 658 (2010), p.141.

Google Scholar

[9] Y. Sun, W.D. Zeng, Y.Q. Zhao, et al: Computational Mater. Sci. Vol. 48 (2010), p.686.

Google Scholar