Predictions of toughness and hardness by using chemical composition and tensile properties in microalloyed line pipe steels

Artificial neural networks with multilayer feed forward topology and back propagation algorithm containing two hidden layers are implemented to predict the effect of chemical composition and tensile properties on the both impact toughness and hardness of microalloyed API X70 line pipe steels. The chemical compositions in the forms of “carbon equivalent based on the International Institute of Welding equation (CEIIW)”, “carbon equivalent based on the Ito-Bessyo equation (CEPcm)”, “the sum of niobium, vanadium and titanium concentrations (VTiNb)”, “the sum of niobium and vanadium concentrations (NbV)” and “the sum of chromium, molybdenum, nickel and copper concentrations (CrMoNiCu)”, as well as, tensile properties of “yield strength (YS)”, “ultimate tensile strength (UTS)” and “elongation (El)” are considered together as input parameters of networks while Vickers microhardness with 10 kgf applied load (HV10) and Charpy impact energy at −10 °C (CVN −10 °C) are assumed as the outputs of constructed models. For the purpose of constructing the models, 104 different measurements are performed and gathered data from examinations are randomly divided into training, testing and validating sets. Scatter plots and statistical criteria of “absolute fraction of variance (R2)”, and “mean relative error (MRE)” are used to evaluate the prediction performance and universality of the developed models. Based on analyses, the proposed models can be further used in practical applications and thermo-mechanical manufacturing processes of microalloyed steels.

[1]  Hao Sun,et al.  Mechanical Properties and Microstructure of X120 Grade High Strength Pipeline Steel , 2010 .

[2]  Eg Kelly,et al.  The metallurgy of welding , 1964 .

[3]  Min Zhou,et al.  Relationship Among Microstructure and Properties and Heat Treatment Process of Ultra-High Strength X120 Pipeline Steel , 2011 .

[4]  G. Zhu,et al.  Effect of Crystallographic Texture on Anisotropy of Yield Strength in X100 Pipeline Steel , 2013 .

[5]  Ali Nazari,et al.  Toughness prediction in functionally graded Al6061/SiCp composites produced by roll-bonding , 2014 .

[6]  Gholamreza Khalaj,et al.  MODELING THE CORRELATION BETWEEN HEAT TREATMENT, CHEMICAL COMPOSITION AND BAINITE FRACTION OF PIPELINE STEELS BY MEANS OF ARTIFICIAL NEURAL NETWORKS , 2013 .

[7]  Ali Nazari,et al.  PREDICTION OF MARTENSITE FRACTION OF MICROALLOYED STEEL BY ARTIFICIAL NEURAL NETWORKS , 2013 .

[8]  J. D. Boyd,et al.  Effects of thermomechanical processing on the microstructure and mechanical properties of a Ti-V-N steel , 1988 .

[9]  J. Szpunar,et al.  Texture and mechanical properties of API X100 steel manufactured under various thermomechanical cycles , 2012 .

[10]  Bert Verlinden,et al.  Thermo-Mechanical Processing of Metallic Materials , 2014 .

[11]  M. R. Toroghinejad,et al.  Modeling the yield strength of hot strip low carbon steels by artificial neural network , 2009 .

[12]  Reza Tafteh,et al.  Austenite decomposition in an X80 linepipe steel , 2011 .

[13]  Llewellyn,et al.  Steels: Metallurgy and Applications , 1992 .

[14]  T. Gladman.  The Physical Metallurgy of Microalloyed Steels , 1997 .

[15]  Ali Nazari,et al.  Predicting Charpy impact energy of Al6061/SiC p laminated nanocomposites in crack divider and crack arrester forms , 2013 .

[16]  Kurt Hornik,et al.  Approximation capabilities of multilayer feedforward networks , 1991, Neural Networks.

[17]  R. H. Titran,et al.  Effects of thermomechanical processing on the microstructure and mechanical properties of Nb-1Zr-C alloys , 1996 .

[18]  Gholamreza Khalaj,et al.  Artificial neural networks application to predict the ultimate tensile strength of X70 pipeline steels , 2013, Neural Computing and Applications.

[19]  Yazheng Liu,et al.  Effects of Niobium and Vanadium on Hydrogen-Induced Delayed Fracture in High Strength Spring Steel , 2011 .

[20]  L. Du,et al.  Strengthening Mechanism of a New 700 MPa Hot Rolled High Strength Steel , 2008 .

[21]  Ke Yang,et al.  Challenge of mechanical properties of an acicular ferrite pipeline steel , 2006 .

[22]  Jae-il Jang,et al.  Fracture toughness anisotropy in a API steel line-pipe , 2007 .

[23]  Ali Nazari,et al.  Artificial neural network to predict the effect of heat treatments on Vickers microhardness of low-carbon Nb microalloyed steels , 2013, Neural Computing and Applications.

[24]  지노 팔룸보.  Thermomechanical processing of metallic materials , 1993 .

[25]  S. Shanmugam,et al.  High strength microalloyed CMn(V–Nb–Ti) and CMn(V–Nb) pipeline steels processed through CSP thin-slab technology: Microstructure, precipitation and mechanical properties , 2006 .

[26]  Vladimir B. Ginzburg,et al.  Flat Rolling Fundamentals , 2000 .

[27]  Download Here,et al.  Annual Book Of Astm Standards , 2014 .