Modeling Mechanical Properties of Low Carbon Hot Rolled Steels

  • N. S. Reddy
  • B. B. Panigrahi
  • J. Krishnaiah
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 202)


Steel is the most important material and it has several applications, and positions second to cement in its consumption in the world. The mechanical properties of steels are very important and vary significantly due to heat treatment, mechanical treatment, processing and alloying elements. The relationships between these parameters are complex, and nonlinear in nature. An artificial neural networks (ANN) model has been used for the prediction of mechanical properties of low alloy steels. The input parameters of the model consist of alloy composition (Al, Al soluble, C, Cr, Cu, Mn, Mo, Nb, Ni, P, S, Si, Ti, V and Nitrogen in ppm) and process parameters (coil target temperature, finish rolling temperature) and the outputs are ultimate tensile strength, yield strength, and percentage elongation. The model can be used to calculate properties of low alloy steels as a function of alloy composition and process parameters at new instances. The influence of inputs on properties of steels is simulated using the model. The results are in agreement with existing experimental knowledge. The developed model can be used as a guide for further alloy development.


Artificial neural networks Low carbon steels Mechanical properties Process parameters 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. Reddy, N.S.: Study of some complex metallurgical systems by computational intelligence techniques. Department of Materials Science and Engineering, vol. Ph.D Thesis, pp. 195. Indian Institute of Technology, Kharagpur, India (2004).Google Scholar
  2. Reddy, N.S., Dzhebyan, I., Lee, J.S., Koo, Y.M.: Modelling of Cr2 N age-precipitation in high nitrogen stainless steels by neural networks. ISIJ International 50, 279-285 (2010).Google Scholar
  3. Reddy, N.S., Krishnaiah, J., Hong, S.G., Lee, J.S.: Modeling medium carbon steels by using artificial neural networks. Materials Science and Engineering A 508, 93-105 (2009).Google Scholar
  4. Bhadeshia, H.K.D.H.: Neural Networks in Materials Science. ISIJ International, Vol. 39,, 966-979 (1999).Google Scholar
  5. Sourmail, T., Bhadeshia, H.K.D.H., mackay, D.J.C.: Neural network model of creep strength of austenitic stainless steels. Materials Science and Technology 18, 655-663 (2002).Google Scholar
  6. Aoyama, T., Suzuki, Y., Ichikawa, H.: Neural networks applied to structure-activity relationships. Journal of Medicinal Chemistry 33, 905 (1990).Google Scholar
  7. Aoyama, T., Suzuki, Y., Ichikawa, H.: Neural networks applied to quantitative structure-activity relationship analysis. Journal of Medicinal Chemistry 33, 2583 (1990).Google Scholar
  8. Rumelhart, D.E., Durbin, R., Golden, R., Chauvin, Y.: Backpropagation: The basic theory. Backpropagation: Theory, Architectures, and Applications 1 (1995).Google Scholar
  9. Lippmann, R.P.: Introduction to computing with neural nets. IEEE ASSP magazine 4, 4 (1987).Google Scholar
  10. Dayhoff, J.E.: Neural Network Architectures: An Introduction. VNR Press, New York (1990).Google Scholar
  11. Zurada, J.M.: Introduction to Artificial Neural Systems. PWS publishing Company, Boston (1992).Google Scholar
  12. Werbos, P.J.: Backpropagation: Basics and new developments.. MIT Press, Cambridge, MA, USA (1998).Google Scholar
  13. Reddy, N.S., Lee, C.S., Kim, J.H., Semiatin, S.L.: Determination of the beta-approach curve and beta-transus temperature for titanium alloys using sensitivity analysis of a trained neural network. Materials Science and Engineering: A 434, 218-226 (2006).Google Scholar
  14. Reddy, N.S., Lee, Y.H., Kim, J.H., Lee, C.S.: High temperature deformation behavior of Ti-6Al-4 V alloy with an equiaxed microstructure: A neural networks analysis. Metals and Materials International 14, 213-221 (2008).Google Scholar
  15. Reddy, N.S., Lee, Y.H., Park, C.H., Lee, C.S.: Prediction of flow stress in Ti-6Al-4 V alloy with an equiaxed α + β microstructure by artificial neural networks. Materials Science and Engineering A 492, 276-282 (2008).Google Scholar
  16. Bhadeshia, H.K.D.H.: Design of ferritic creep-resistant steels. ISIJ International 41, 626 (2001).Google Scholar
  17. Bhadeshia, H.K.D.H.: Design of Creep Resistant Welding Alloys. In: ASM Proceedings of the International Conference: Trends in Welding Research, pp. 795-804. (Year).Google Scholar
  18. Bhadeshia, H.K.D.H.: Modelling of steel welds. Materials Science and Technology 8, 123-133 (1992).Google Scholar

Copyright information

© Springer India 2013

Authors and Affiliations

  1. 1.School of Materials Science and EngineeringGyeongsang National UniversityJinjuKorea
  2. 2.Department of Materials Science and EngineeringIndian Institute of TechnologyHyderabadIndia
  3. 3.Bharat Heavy Electrical LimitedTrichyIndia

Personalised recommendations