Artificial neural networks application to predict the ultimate tensile strength of X70 pipeline steels

[1]  Ali Nazari,et al.  RETRACTED: Microhardness profile prediction of functionally graded steels by artificial neural networks , 2013 .

[2]  Gholamreza Khalaj,et al.  ANN-based prediction of ferrite fraction in continuous cooling of microalloyed steels , 2012, Neural Computing and Applications.

[3]  Ali Nazari,et al.  Application of artificial neural networks for analytical modeling of Charpy impact energy of functionally graded steels , 2011, Neural Computing and Applications.

[4]  Ali Nazari,et al.  RETRACTED ARTICLE: Artificial neural networks to prediction total specific pore volume of geopolymers produced from waste ashes , 2011, Neural Computing and Applications.

[5]  Ali Nazari,et al.  Modeling ductile to brittle transition temperature of functionally graded steels by artificial neural networks , 2011 .

[6]  Ali Nazari,et al.  Prediction split tensile strength and water permeability of high strength concrete containing TiO2 nanoparticles by artificial neural network and genetic programming , 2011 .

[7]  Amir Parsapour,et al.  Analysis of the effects of processing parameters on mechanical properties and formability of cold rolled low carbon steel sheets using neural networks , 2010 .

[8]  I. Rezić,et al.  Estimation of Steel Guitar Strings Corrosion by Artificial Neural Network , 2010 .

[9]  Dierk Raabe,et al.  Prediction of cold rolling texture of steels using an Artificial Neural Network , 2009 .

[10]  H. K. D. H. Bhadeshia,et al.  Performance of neural networks in materials science , 2009 .

[11]  S. Singh,et al.  Artificial Neural Network: Some Applications in Physical Metallurgy of Steels , 2009 .

[12]  S. Shanmugam,et al.  Microstructure and high strength–toughness combination of a new 700 MPa Nb-microalloyed pipeline steel , 2008 .

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

[14]  Ridha Amamou,et al.  Ground surface roughness prediction based upon experimental design and neural network models , 2006 .

[15]  Ke Yang,et al.  In situ TEM study of the effect of M/A films at grain boundaries on crack propagation in an ultra-fine acicular ferrite pipeline steel , 2006 .

[16]  R. Pérez,et al.  Slow strain rate corrosion and fracture characteristics of X-52 and X-70 pipeline steels , 2005 .

[17]  K. R. Ramakrishnan,et al.  Neural network analysis for corrosion of steel in concrete , 2005 .

[18]  L. A. Dobrzański,et al.  Application of neural networks for designing the chemical composition of steel with the assumed hardness after cooling from the austenitising temperature , 2005 .

[19]  Ke Yang,et al.  Strengthening and improvement of sulfide stress cracking resistance in acicular ferrite pipeline steels by nano-sized carbonitrides , 2005 .

[20]  Tuğrul Özel,et al.  Predictive modeling of surface roughness and tool wear in hard turning using regression and neural networks , 2005 .

[21]  José C. G. Teixeira,et al.  High-strength steel development for pipelines: A brazilian perspective , 2005 .

[22]  Xie Changsheng,et al.  Influence of Mo content on microstructure and mechanical properties of high strength pipeline steel , 2004 .

[23]  Ragip Ince,et al.  Prediction of fracture parameters of concrete by Artificial Neural Networks , 2004 .

[24]  S. K. Kim,et al.  Effect of microstructure on the yield ratio and low temperature toughness of linepipe steels , 2002 .

[25]  Ke Yang,et al.  The effects of thermo-mechanical control process on microstructures and mechanical properties of a commercial pipeline steel , 2002 .

[26]  A. K. Tieu,et al.  Influence of Nb, V and Ti on peak strain of deformed austenite in Mo-based micro-alloyed steels , 2002 .

[27]  Jin H. Huang,et al.  Detection of cracks using neural networks and computational mechanics , 2002 .

[28]  M. Gräf.  Development and production of high strength pipeline steels , 2001 .

[29]  Kijoon H. P. Kim,et al.  Influence of Mo on precipitation hardening in hot rolled HSLA steels containing Nb , 2000 .

[30]  Abhijit Mukherjee,et al.  Artificial neural networks in prediction of mechanical behavior of concrete at high temperature , 1997 .

[31]  A. Takahashi,et al.  Influence of Microhardness and Inclusion on Stress Oriented Hydrogen Induced Cracking of Line Pipe Steels , 1996 .

[32]  A. Takahashi,et al.  Microstructural Refinement by Cu Addition and Its Effect on Strengthening and Toughening of Sour Service Line Pipe Steels , 1996 .

[33]  A. Takahashi,et al.  Thermo-mechanical Control Process as a Tool to Grain-refine the Low Manganese Containing Steel for Sour Service Line Pipe , 1996 .

[34]  S. Suzuki,et al.  Review of mechanical and metallurgical investigations of martensite-austenite constituent in welded joints in Japan , 1996 .

[35]  T W Montemarano,et al.  HIGH STRENGTH LOW ALLOY STEELS IN NAVAL CONSTRUCTION , 1986 .

[36]  Geoffrey E. Hinton,et al.  Learning internal representations by error propagation , 1986 .

[37]  James L. McClelland,et al.  Parallel distributed processing: explorations in the microstructure of cognition, vol. 1: foundations , 1986 .

[38]  James A. Anderson,et al.  Cognitive and psychological computation with neural models , 1983, IEEE Transactions on Systems, Man, and Cybernetics.

[39]  J J Hopfield,et al.  Neural networks and physical systems with emergent collective computational abilities. , 1982, Proceedings of the National Academy of Sciences of the United States of America.

[40]  F. B. Pickering,et al.  Physical metallurgy and the design of steels , 1978 .

[41]  Frank Rosenblatt,et al.  PRINCIPLES OF NEURODYNAMICS. PERCEPTRONS AND THE THEORY OF BRAIN MECHANISMS , 1963 .