PREDICTION OF MARTENSITE FRACTION OF MICROALLOYED STEEL BY ARTIFICIAL NEURAL NETWORKS

The final microstructure and resulting mechanical properties in the linepipe steels are predominantly determined by austenite decomposition during cooling after thermomechanical and welding processes. The paper presents some results of the research connected with the development of a new approach based on the artificial neural network to predicting the martensite fraction of the phase constituents occurring in five microalloyed steels after continuous cooling. The in- dependent variables in the model are chemical compositions, niobium condition, austenitizing temperature, initial austenite grain size and cooling rate over the temperature range of the occurrence of phase transformations. For the purpose of constructing these models, 104 different experimental data were gathered from the literature. According to the input parameters in feedforward backpropagation algo- rithm, the constructed networks were trained, validated and tested. In this model, the training and testing results in the artificial neural network have shown a strong potential for prediction of effects of chemical compositions and heat treatments on phase transformation of microalloyed steels.

[1]  J. M. Rodriguez-Ibabe,et al.  Effect of austenite microstructure and cooling rate on transformation characteristics in a low carbon Nb–V microalloyed steel , 2011 .

[2]  E. Palmiere,et al.  Compositional and microstructural changes which attend reheating and grain coarsening in steels containing niobium , 1994 .

[3]  Y. Chen.  MICROSTRUCTURE AND MECHANICAL PROPERTY DEVELOPMENT IN THE SIMULATED HEAT AFFECTED ZONE OF V TREATED HSLA STEELS , 2006 .

[4]  Mohammad Bagher Tavakoli,et al.  Modified Levenberg-Marquardt Method for Neural Networks Training , 2007 .

[5]  Øystein Grong,et al.  Metallurgical modelling of welding , 1994 .

[6]  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.

[7]  Ibrahim H. Guzelbey,et al.  Prediction of web crippling strength of cold-formed steel sheetings using neural networks , 2006 .

[8]  Silvia Curteanu,et al.  Direct and inverse neural networks modelling applied to study the influence of the gas diffusion layer properties on PBI-based PEM fuel cells , 2010 .

[9]  John Norrish,et al.  Artificial neural networks for modelling the mechanical properties of steels in various applications , 2005 .

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

[11]  Di Wu,et al.  Artificial neural network modeling of microstructure during C-Mn and HSLA plate rolling , 2009 .

[12]  J. Sobhani,et al.  Prediction of the compressive strength of no-slump concrete: A comparative study of regression, neural network and ANFIS models , 2010 .

[13]  Sybrand van der Zwaag,et al.  Effects of Carbon Concentration and Cooling Rate on Continuous Cooling Transformations Predicted by Artificial Neural Network , 1999 .

[14]  Sepehr Gerami,et al.  Characterization and microstructural evolution model of a Nb ferrite-bainite steel , 2010 .

[15]  İlker Bekir Topçu,et al.  Prediction of long-term effects of GGBFS on compressive strength of concrete by artificial neural networks and fuzzy logic , 2009 .

[16]  Plamen Petkov,et al.  Austenite decomposition of low carbon high strength steels during continuous cooling , 2004 .

[17]  Kulwinder Kaur Lottey.  Austenite decomposition of a HSLA-Nb/Ti steel and an A1-TRIP steel during continuous cooling , 2004 .

[18]  D. Kwon,et al.  Correlation of microstructure and fracture properties in weld heat- affected zones of thermomechanically controlled processed steels , 1992 .

[19]  Lars-Erik Svensson,et al.  Control of Microstructures and Properties in Steel Arc Welds , 1994 .

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

[21]  K. Easterling.  Introduction to the physical metallurgy of welding , 1983 .

[22]  Okan Karahan,et al.  Comparison of artificial neural network and fuzzy logic models for prediction of long-term compressive strength of silica fume concrete , 2009, Adv. Eng. Softw..