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Ironmaking & Steelmaking
Processes, Products and Applications
Volume 48, 2021 - Issue 2
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Research Article

Mathematical modelling for predicting mechanical properties in rebar manufacturing

, , , &
Pages 161-169 | Received 25 Feb 2020, Accepted 25 Mar 2020, Published online: 19 Apr 2020

References

  • Jones JA, Bowman B, Lefrank P. Electric arc furnace steelmaking. Steelmaking and refining volume. Pittsburgh, PA: The AIST Steel Foundation; 1998.
  • Bekker J, Craig I, Pistorius P. Model predictive control of an electric arc furnace off-gas process. Control Eng Pract. 2000;8:445–455. doi: 10.1016/S0967-0661(99)00163-X
  • Yildirim IZ, Prezzi M. Experimental evaluation of eaf ladle steel slag as a geo-fill material: mineralogical, physical & mechanical properties. Constr Build Mater. 2017;154:23–33. doi: 10.1016/j.conbuildmat.2017.07.149
  • Sampaio RS, Jones J, Vieira J. Hot metal strategies for the EAF industry. Iron & Steel Technol. 2009;6:31–37.
  • Boër CR, Rebelo NM, Rydstad HA, et al. Process modelling of metal forming and thermomechanical treatment. Heidelberg: Springer Science & Business Media; 1986.
  • Worrell E, Van Gent P, Neelis M, et al. Energy efficiency improvement and cost saving opportunities for the U.S. iron and steel industry an ENERGY STAR(R) guide for energy and plant managersErnest Orlando Lawrence Berkeley National Laboratory; 2010.
  • Gigović-Gekić A, Oruč M, Avdušinović H, et al. Regression analysis of the influence of a chemical composition on the mechanical properties of the steel nitronic 60. Mater Technol. 2014;48:433–437.
  • Juutilainen I, Röning J, Myllykoski L. Modelling the strength of steel plates using regression analysis and neural networks. Proceedings of International Conference on Computational Intelligence for Modelling, Control and Automation; p. 681–691. 2003.
  • Ghaisari J, Jannesari H, Vatani M. Artificial neural network predictors for mechanical properties of cold rolling products. Advances in Engineering Software. 2012;45:91–99. doi: 10.1016/j.advengsoft.2011.09.016
  • Lalam S, Tiwari PK, Sahoo S, et al. Online prediction and monitoring of mechanical properties of industrial galvanised steel coils using neural networks. Ironmaking & Steelmaking. 2019;46:89–96. doi: 10.1080/03019233.2017.1342424
  • Yang YY, Mahfouf M, Linkens DA, et al. Tensile strength prediction for hot rolled steels by Bayesian neural network model. IFAC Proceedings Volumes. 2009;42:255–260. doi: 10.3182/20091014-3-CL-4011.00046
  • Belayadi A, Bourahla B. Neural network model for 7000 (al-z) alloys: classification and prediction of mechanical properties. Physica B: Condensed Matter. 2019;554:114–120. doi: 10.1016/j.physb.2018.11.012
  • Boljanovic V. Metal shaping processes: casting and molding, particulate processing, deformation processes, and metal removal. New York: Industrial Press Inc; 2010.
  • Semiatin S. ASM metals handbook 14: forming and forging. Novelty (OH): ASM International; 1993.
  • Roberts WL. Hot rolling of steel. Boca Raton (FL): CRC Press; 1983.
  • Augusti A. In-line thermal treatment applies to straight and coiled rebars. Steel Times International. 1995;19:44.
  • Cadoni E, Dotta M, Forni D, et al. Mechanical behaviour of quenched and self-tempered reinforcing steel in tension under high strain rate. Mater Des. 2013;49:657–666. doi: 10.1016/j.matdes.2013.02.008
  • Madias J, Wright M, Wolkowicz P. Reinforcing bar: Hardening mechanisms and performance in use. In: 2016 AISTech Conference Proceedings. Pittsburgh: Association for Iron Steel Technology; 2016. p. 2287–2296.
  • Maalekian M. 2007. The effects of alloying elements on steels (I). Technical Report Institute of Materials Science, Joining and Forming.
  • Takaki S, Fujioka M, Aihara S, et al. Effect of copper on tensile properties and grain-refinement of steel and its relation to precipitation behavior. Mater Trans. 2004;45:2239–2244. doi: 10.2320/matertrans.45.2239
  • Montgomery DC, Peck EA, Vining GG. Introduction to linear regression analysis. 6th ed. Hoboken, NJ: Wiley & Sons; 2006.
  • White H. Using least squares to approximate unknown regression functions. Int Econ Rev (Philadelphia). 1980;21:149–170. doi: 10.2307/2526245
  • Montgomery DC, Runger GC. Applied statistics and probability for engineers. 5th ed. Hoboken, NJ: John Wiley & Sons Inc; 2003.
  • Haykin SS. Neural networks and learning machines. 3rd ed. Hamilton: Pearson Education; 2009.
  • Kurková V. Kolmogorov's theorem and multilayer neural networks. Neural Netw. 1992;5:501–506. doi: 10.1016/0893-6080(92)90012-8
  • Singh S, Bhadeshia H, MacKay D, et al. Neural network analysis of steel plate processing. Ironmaking and Steelmaking. 1998;25:355–365.
  • Amin RK, Pickering FB. Austenite grain coarsening and the effect of thermomechanical processing on austeniterecrystallization. International Conference on the Thermomechanical Processing of Microalloyed Austenite. Warrendale: The Metallurgical Society of AIME; 1981. Vol. 17, p. 1–31.
  • Honeycombe RWK. Steels: microstructure and properties. Oxford, UK: Edward Arnold; 1981.
  • Hosford WF. Mechanical behavior of materials. New york: Cambridge University Press; 2010.

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