低碳型钢中添加ZrC粒子获得超细晶粒的研究

通过在低碳型钢中加入的第二相粒子 Zr C,研究了 Zr C粒子体积分数对低碳型钢组织和力学性能的影响。研究结果表明 :当轧制变形量为 84% ,Zr C粒子的加入量为 0 .8%时 ,轧后水冷可获得超细组织 ,晶粒尺寸可达 9.8μm。随着 Zr C粒子体积分数的增加 ,晶粒尺寸减小 。

新型低碳Mn-Mo-Nb-Cu-V型机械结构用高强钢的热处理工艺优化

采用不同工艺对新型低碳Mn-Mo-Nb-Cu-V型机械结构用高强钢试样进行了热处理,并进行了试样冲击性能和拉伸性能的测试与分析。结果表明,在试验条件下,随淬火温度或回火温度不断上升,试样的冲击吸收功、抗拉强度、屈服强度和断后伸长率先增大后减小,试样冲击性能和拉伸性能先提高后下降。新型低碳Mn-Mo-Nb-Cu-V型高强机械结构用钢的最佳热处理工艺参数为：920℃×15 min淬火和710℃×6 h回火。

新型低碳含铌热轧H型钢的热变形行为

利用热压缩试验、显微组织分析等手段,研究了一种新型低碳含铌热轧H型钢在1000~1200℃变形温度和0.1~5 s^(-1)应变速率下的热变形行为。分析了变形参数对试验钢微观组织的影响,建立了耦合应变量因素的改进型本构方程,并采用临界比的临界应变模型对发生动态再结晶的临界应变值进行了预测。结果表明:较低应变速率和变形温度下,试验钢的原始奥氏体组织更均匀且平均晶粒尺寸更小;应变速率的升高不利于动态再结晶的发生。发生动态再结晶的临界应变与峰值应变的关系为ε_(c)/ε_(p)=0.47。与耦合应变量因素有关的本构方程和临界应变预测模型能较准确地预测各变形温度下低碳含铌热轧H型钢的流变应力和动态再结晶临界应变值。

An optimized hardness model for carburizing-quenching of low carbon alloy steel

Research has been conducted about the hardness prediction for the carburizing and quenching process based on an optimized hardness simulation model,in accordance with the calculation rule of mixed phases.The coupling field model incorporates carburizing field analysis,temperature field analysis,phase transformation kinetics analysis and a modified hardness calculation model.In determination of the calculation model for hardness,calculation equations are given to be applied to low carbon content(x(C)<0.5%) for the child phases and the martensite hardness is calculated for high carbon content(x(C)>0.5%) in alloy.Then,the complete carburizing-quenching hardness calculation model is built,and the hardness simulation data are corrected considering the influence of residual austenite(RA) on hardness.Hardness simulations of the carburizing and quenching process of 17CrNiMo6 samples have been performed using DEFORM-HT_V10.2 and MATLAB R2013 a.Finally,a series of comparisons of simulation results and measured values show a good agreement between them,which validates the accuracy of the proposed mathematical model.

Effect of coating material and lubricant on forming force and surface defects in wire drawing process

A pilot wire drawing machine as well as wire end-pointing roller was developed. Using these machines, a wire drawing test for four different coating materials and two different lubricants was performed as the reduction ratio increased from 10% to 30%. Materials used for a substrate in this study are plain carbon steel （AIS11045） and ultra low carbon bainite steel. To compute the friction coefficient between the coating layer of wire and the surface of die lbr a specific lubricant, a series of finite element analyses were carried out. SEM observations were also conducted to investigate the surface defects of wire deformed. Results show that the behavior of drawing force varies with the lubricant-type at the initial stage of drawing. The powder-typed lubricant with a large particle causes the retardation of lull lubrication on the entire contact surface and the local delamination of coating layer on the wire surface. As the flow stress of a substrate increases, the delamination becomes severe.

Artificial neural network prediction of mechanical properties of hot rolled low carbon steel strip

Conventionally, direct tensile tests are employed to measure mechanical properties of industrially pro- duced products. In mass production, the cost of sampling and labor is high, which leads to an increase of total pro- duction cost and a decrease of production efficiency. The main purpose of this paper is to develop an intelligent pro- gram based on artificial neural network （ANN） to predict the mechanical properties of a commercial grade hot rolled low carbon steel strip, SPHC. A neural network model was developed by using 7 x 5 x 1 back-propagation （BP） neural network structure to determine the multiple relationships among chemical composition, product pro- cess and mechanical properties. Industrial on-line application of the model indicated that prediction results were in good agreement with measured values. It showed that 99.2 % of the products＇ tensile strength was accurately pre- dicted within an error margin of ~ 10 %, compared to measured values. Based on the model, the effects of chemical composition and hot rolling process on mechanical properties were derived and the relative importance of each in- put parameter was evaluated by sensitivity analysis. All the results demonstrate that the developed ANN models are capable of accurate predictions under real-time industrial conditions. The developed model can be used to sub- stitute mechanical property measurement and therefore reduce cost of production. It can also be used to control and optimize mechanical properties of the investigated steel.

Effect of Cu on the boron segregation at grain boundaries and vacancy-type defects in ultra-low carbon micro-alloy steels

By thermal neutron irradiation particle tracking autoradiography(PTA)technique,the development of boron segregation at grain boundaries in ultra-low carbon micro-alloy steels was investigated during cooling from 1150°C to 850°C,and the effect of Cu on boron segregation at grain boundaries was discussed.By positron annihilation lifetime(PAL)technique,the changes of vacancy-type defects with temperatures and the effect of Cu on vacancy-type defects in the cooling process were discussed.Results show that,the concentration of boron at grain boundaries increases rapidly at the beginning of the cooling;after that,it begins to decrease;and then,it increases gradually again.The addition of Cu not only increases the concentration of boron at grain boundaries but also speeds up the development process of boron segregation at grain boundaries.During the continuous cooling process,the addition of Cu significantly affects the change of vacancy-type defects with temperatures in ultra-low carbon micro-alloy steels.