Research On The Microdtructure And Model Of Low Carbon Bainitic Steel In TMCP

With the development of industrial technology, the requirements of industrial materials are also increasingly improved.The Low Carbon Bainite is a new material as the21st century,it is a new steel line with high strength,high toughness,excellent welding perfomance,high wear-resistant and high anti-fatigue performance that has developed in recent20years, its the good comprehensive performance make the low carbon steel has widely used in each large domain. Study the properties of Low Carbon Bainitic steel,the main determining is the microstructure,the evolution of the microstructure directly affects the properites of materials, and the evolution of the microstructure through the deformation process to make its implementation,such as TMCP etc. So establish a model between the process of parameters and the variation of microstructure is very necessary.Research the influence of deformation and microstructure for Low Carbon Bainitic,and establish a prediction model of the Bainite.The mainly TMCP process of this experiment is controlled rolling and controlled cooling and tempering treatment. Low Carbon Bainite steel was rolled8times on the experimental mill, the terminal temperature was850℃. Water cooling, oil cooling and air cooling was taken after the deforming, the structure was thinning fully by control the deformation and cooling rate. Tempering was conducted after the cooling to remove internal stress and arise the steady of structure. The tempering temperature was room temperature,400℃,500℃,600℃Analysis the processed of Low Carbon Bainitic steel microstructure by the metallographic analysis technical,research the influence rule between the process and microstructure;with the experimental data and the knowledge of Network,established a prediction model with the data of microstructure as output,the deformation process parameters as input,then learn and train the model with the experimental data.The following conclusions are reached:(1) Contrast Analysis the microstructure on controlled rolling,controlled cooling and temper treatment,its revealed that the effect of cooling rate is more than final cooling temperature and temper temperature. (2)The cooling speed on microstructure influence rule is:water cooling after controlled rolling,the mainly microstructure is banded Bainite structure;oil cooling after controlled rolling,the mainly microstructure is banded Bainite structure,granular Bainite structure and ferrite;air cooling after controlled rolling,the microstructure is granular Bainite structure and ferrite.(3)Use the Genetic Algorithm changing the threshold,weights for Network Optimization,and to improve the precision of the model.(4) To establish a3×1×1BP network Bainite organization prediction model with three layers through the results of contrast test,the cooling speed,final cooling temperature, tempering temperature as input, Baitine grain size as output.Then through the experimental data to train the BP network,and inspect the model of training successed.The inspection results show that this neural network model has high prediction accuracy with the prediction accuracy under10%,and most of them at around5%. Judging from this,the prediction model has higher precision.