[1] 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.
[2] 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.
[3] K.U. Kainer, Magnesium—Alloys and Technology, Wiley-VCH, Germany, (2003).
[4] 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.
[5] 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.
[6] 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.
[7] 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.
[8] 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.
[9] P.J. Zerilli, R.W. Armstrong, Dislocation-mechanics-based constitutive relations for material dynamics calculations, Journal of Applied Physics 61 (1987) 1816–1825.
[10] 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.
[11] 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.
[12] 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.
[13] 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.
[14] 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.
[15] 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.
[16] M. Rakhshkhorshid, S.H. Hashemi, Experimental study of hot deformation behavior in API X65 steel, Mater. Sci. Eng., A 573, (2013) 37–44.
[17] M. Shaban, B. Eghbali, Determination of critical conditions for dynamic recrystallization of a microalloyed steel, Mater. Sci. Eng., A 527, (2010) 4320–4325.
[18] 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).
[19] 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.
[20] C. Campbell, Kernel methods: a survey of current techniques, Neurocomputing 48 (2002) 63–84.
[21] V.N. Vapnik, The Nature of Statistical learning Theory, Springer, New York, (1995).
[22] A.J. Smola, B. Scholkopf, A tutorial on support vector regression, Stat. Comput. 14 (3) (2004) 199– 222.
[23] F. Parrella, Online support vector regression, A thesis presented for the degree of Information Science, University of Genoa, Italy, (2007).