L. Yang, H. Hou, Y.H. Zhao, X.M Yang, Effect of applied pressure on microstructure and mechanical properties of Mg-Zn-Y quasicrystal-reinforced AZ91D magnesium matrix composites prepared by squeeze casting, Trans. Nonferrous Met. Soc. China 25 (2015) 3936-3943.
 Y. Li, Y. Chen, H. Cui, J. Ding, L. Zuo, J. Zhang, Hot deformation behavior of a spray-deposited AZ31 magnesium alloy, Rare Metals 28 (2009) 91–97.
 K.U. Kainer, Magnesium—Alloys and Technology, Wiley-VCH, Germany, (2003).
 S.J. Liang, Z.Y. Liu, E.D. Wang, Mechanical properties and texture evolution during rolling process of an AZ31 Mg alloy, Materials Letters 62 (2008) 3051–3054.
 Y.C. Lin, X.M. Chen, A critical review of experimental results and constitutive descriptions for metals and alloys in hot working, Materials and Design 32 (2011) 1733–1759.
 L. Gambirasio, E. Rizzi, On the calibration strategies of the Johnson–Cook strength model: Discussion and applications to experimental data, Materials Science and Engineering: A 610 (2014) 370–413.
 Z. Akbari, H. Mirzadeh, J.M. Cabrera, A simple constitutive model for predicting flow stress of medium carbon microalloyed steel during hot deformation, Materials and Design 77 (2015) 126–131.
 A. Abbasi-Bani, A. Zarei-Hanzaki, M.H. Pishbin, N. Haghdadi, A comparative study on the capability of Johnson–Cook and Arrhenius-type constitutive equations to describe the flow behavior of Mg–6Al–1Zn alloy, Mechanics of Materials 71 (2014) 52-61.
 P.J. Zerilli, R.W. Armstrong, Dislocation-mechanics-based constitutive relations for material dynamics calculations, Journal of Applied Physics 61 (1987) 1816–1825.
 Y.C. Lin, M.S. Chen, J. Zhang, Constitutive modeling for elevated temperature flow behavior of 42CrMo steel, Computational Materials Science 424 (2008) 470–477.
 D.L. Preston, D.L. Tonks, D.C. Wallace, Model of plastic deformation for extreme loading conditions, Journal of Applied Physics 93 (2003) 211–20.
 H. Mirzadeh, J.M. Cabrera, J.M. Prado, A. Najafizadeh, Modeling and prediction of hot deformation flow curves, Metallurgical and Materials Transactions A 43 (2012) 108–123.
 R.K. Desu, S.C. Guntuku, A. Balu, A.K. Gupta, Support Vector Regression based Flow Stress Prediction in Austenitic Stainless Steel 304, Procedia Materials Science 6 ( 2014 ) 368 – 375.
 M. Rakhshkhorshid, A.R. Maldar, A comparative study on constitutive modeling of hot deformation flow curves in AZ91 magnesium alloy, Iranian Journal of Materials Forming 3(1) (2016) 27-37.
 G.R. Ebrahimi, A.R. Maldar, R. Ebrahimi, A. Davoodi, Effect of thermomechanical parameters on dynamically recrystallized grain size of AZ91 magnesium alloy, J. Alloys Compd. 509 (2011) 2703– 2708.
 M. Rakhshkhorshid, S.H. Hashemi, Experimental study of hot deformation behavior in API X65 steel, Mater. Sci. Eng., A 573, (2013) 37–44.
 M. Shaban, B. Eghbali, Determination of critical conditions for dynamic recrystallization of a microalloyed steel, Mater. Sci. Eng., A 527, (2010) 4320–4325.
 V. N. Vapnik, Statistical learning theory. In S. Haykin (Ed.), Adaptive and learning systems for signal processing, communications and control. John Wiley and Sons, (1998).
 B. Lela, D. Bajić, S. Jozić, Regression analysis, support vector machines, and Bayesian neural network approaches to modeling surface roughness in face milling, Int J Adv Manuf Technol 42 (2009) 1082–1088.
 C. Campbell, Kernel methods: a survey of current techniques, Neurocomputing 48 (2002) 63–84.
 V.N. Vapnik, The Nature of Statistical learning Theory, Springer, New York, (1995).
 A.J. Smola, B. Scholkopf, A tutorial on support vector regression, Stat. Comput. 14 (3) (2004) 199– 222.
 F. Parrella, Online support vector regression, A thesis presented for the degree of Information Science, University of Genoa, Italy, (2007).