热镀锌工艺对无Si含P的TRIP钢力学性能影响

利用光学显微镜、透射电镜、X射线衍射和拉伸试验等方法,分析测试了热镀锌工艺对无Si含P的TRIP钢力学性能和微观组织的影响.结果表明:实验用钢可获得780 MPa以上的抗拉强度和24%以上的断后延伸率.在热镀锌工艺中,两相区加热温度和贝氏体等温温度对钢的力学性能影响较小,而贝氏体等温时间的影响最为显著.当贝氏体等温时间由20 s增加到60 s时,实验用钢的屈服强度上升了65 MPa,抗拉强度下降了45 MPa,延伸率大幅度增加,从23.01%增加到27.56%,出现最佳的综合力学性能.无Si含P热镀锌TRIP钢的微观组织由铁素体、贝氏体、残余奥氏体和马氏体组成,随着贝氏体等温时间的减少,钢中残余奥氏体含量和稳定性降低,相应地,马氏体含量明显增加,实验用钢从典型的TRIP钢力学特征慢慢转变为与双相钢相似的力学特征.     

基于数据特征的加热炉钢温预报模型

针对加热炉工业过程具有复杂、非线性、时滞性的特点和钢坯出炉温度预报问题,提出了一种基于数据特征的改进主元回归(PCR)加热炉钢温预报模型的建立方法.首先通过对原始数据进行同步化处理来解决各数据变量间存在的时间滞后问题;然后提取生产过程中各批次钢坯的统计特征和熵特征,并依据一定顺序将这些特征排列组合,构造等长的数据特征向量;最后通过PCR方法建立过程变量的数据特征和钢坯出炉温度之间的回归预报模型.本文以某钢厂加热炉工业过程为背景进行实验仿真,采用实际生产数据求取建模参数,并对钢坯出炉温度预报进行了测试.实验的校验与误差分析表明,该方法在预测钢坯出炉温度方面具有更好的性能,且预测误差满足工业应用的精度要求.     

高强钢动态力学性能本构模型研究

为了研究高强钢材料在动态加载过程中的力学响应,采用分离式霍普金森压杆对材料进行了不同应变率（3000到 12000s-1）和不同温度（20℃到800℃）单轴压缩实验.实验结果表明：高强钢的动态力学行为受应变率和温度的强烈影响.流动应力随着应变率的升高而增加,随着温度的升高而降低.提出了一个经验型本构模型来描述材料的加工硬化和温度软化行为.该本构模型预测的应力——应变曲线与实验结构较好吻合,表明该本构模型可进一步用于高强钢动态变形过程的数值模拟研究.     

LF-RH工艺生产超低硫钢钢水温度预报模型

根据LF-RH精炼超低硫钢的工艺流程,基于冶金热力学、传热和热量平衡等原理,同时考虑钢包的热状态,建立出钢至浇注过程中的钢水温度预报模型。通过该模型可以得到不同时刻钢水温度和钢包内衬温度的分布曲线。实例验证结果显示,模型计算值与实测值的平均误差为5.6℃,误差在±8℃以内的占87.5%。另外,运用该模型分析了钢包热状态、出钢过程、传运时间、RH真空室内壁温度等因素对钢水温度的影响规律,可为超低硫钢精炼工艺的优化提供参考。     

基于粒子群优化的神经网络漏钢预报模型研究

针对BP神经网络在训练过程中收敛速度慢以及用于模式识别泛化能力差的问题,将粒子群优化算法PSO引入到BP神经网络的训练过程,建立了PSO-BP神经网络模型,并将其应用到连铸漏钢预报系统中。结合某钢厂连铸现场历史数据对该连铸漏钢预报系统进行了测试,测试结果以98.03%的预报率及100%的报出率,验证了基于粒子群优化算法的BP神经网络连铸漏钢预报系统模型的可行性和有效性。     

Effect of hot-dip galvanizing processes on the microstructure and mechanical properties of 600-MPa hot-dip galvanized dual-phase steel

A C-Mn dual-phase steel was soaked at 800℃ for 90 s and then either rapidly cooled to 450℃ and held for 30 s (process A) or rapidly cooled to 350℃ and then reheated to 450℃ (process B) to simulate the hot-dip galvanizing process. The influence of the hot-dip galvanizing process on the microstructure and mechanical properties of 600-MPa hot-dip galvanized dual-phase steel (DP600) was investigated using optical microscopy, scanning electron microscopy (SEM), transmission electron microscopy (TEM), and tensile tests. The results showed that, in the case of process A, the microstructure of DP600 was composed of ferrite, martensite, and a small amount of bainite. The granular bainite was formed in the hot-dip galvanizing stage, and martensite islands were formed in the final cooling stage after hot-dip galvanizing. By contrast, in the case of process B, the microstructure of the DP600 was composed of ferrite, martensite, bainite, and cementite. In addition, compared with the yield strength (YS) of the DP600 annealed by process A, that for the DP600 annealed by process B increased by approximately 50 MPa because of the tempering of the martensite formed during rapid cooling. The work-hardening coefficient (n value) of the DP600 steel annealed by process B clearly decreased because the increase of the YS affected the computation result for the n value. However, the ultimate tensile strength (UTS) and elongation (A₈₀) of the DP600 annealed by process B exhibited less variation compared with those of the DP600 annealed by process A. Therefore, DP600 with excellent comprehensive mechanical properties (YS=362 MPa, UTS=638 MPa, A₈₀=24.3%, n=0.17) was obtained via process A.     

热轧带钢力学性能预测模型及其应用

为实现对热轧带钢的屈服强度、抗拉强度、断裂延伸率等力学性能的预测及控制,利用人工神经网络技术,分别建立了根据生产工艺参数预测力学性能的质量模型,以及根据力学性能要求对生产工艺参数进行优化的逆质量控制模型.利用质量预测模型,分析得出屈服强度随卷取温度的上升而下降的变化规律,进而可以对组织性能进行在线调整,实现在线应用.     

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.     

30SiMnCrB5热成形钢的微观组织和力学性能

为了提高热成形钢的综合性能,设计了一种C-Si-Mn-Cr-B系热成形钢,采用热膨胀仪测定并研究了30Si Mn Cr B5热成形钢的连续冷却转变曲线和相变规律.分析了经轧制、退火及热成形模拟后钢板的微观组织形貌和力学性能,结合等密度线极图的方法,判定了热成形模拟后钢板中马氏体变体与母相的取向关系.30Si Mn Cr B5热成形钢具有较好的淬透性,临界冷速为5℃·s-1,有效抑制了珠光体和贝氏体的形成,完全马氏体组织的硬度可达600 HV以上.热成形模拟后的微观组织由板条马氏体和残余奥氏体构成,残余奥氏体主要以薄膜状分布在马氏体板条间,质量分数为6%~8%,抗拉强度为1800 MPa左右,总伸长率可达10%以上,强度和塑性的匹配较好.热成形模拟后30Si Mn Cr B5热成形钢板中马氏体变体与母相的取向关系更接近N-W关系,12种变体没有都出现在原始奥氏体内.     

Effect of hot stamping parameters on the mechanical properties and microstructure of cold-rolled 22MnB5 steel strips

Thermomechanical experiments were carried out to reproduce the hot stamping process and to investigate the effects of process parameters on the microstructure and mechanical properties of stamped parts. The process parameters, such as austenitizing temperature, soaking time, initial deformation temperature and cooling rate, are studied. The resulting microstructures of specimens were observed and analyzed. To evaluate the mechanical properties of specimens, tensile and hardness tests were also performed at room temperature. The optimum parameters to achieve the highest tensile strength and the desired microstructure were acquired by comparing and analyzing the results. It is indicated that hot deformation changes the transformation characteristics of 22MnB5 steel. Austenite deformation promotes the austenite-to-ferrite transformation and elevates the critical cooling rate to induce a fully martensitic transformation.     

一种超高强度钢组织性能的研究

通过力学测试、金相、扫描电镜观察等方法研究了不同淬火温度下一种淬火 低温回火超高强度钢的组织性能,系统的探讨了未溶相与淬火温度的关系。结果表明：钢采用低于950℃的淬火温度,组织中存在大量未溶碳化物,当温度高于1010℃时,未溶碳化物基本溶解,马氏体板条束明显粗化,对钢的力学性能有害,适宜的淬火温度为1010℃。     

热处理工艺对27SiMn钢力学性能的影响

27SiMn液压支架管经过调质热处理来实现其良好的综合力学性能.采用四因素三水平的热处理正交试验,研究了不同热处理工艺参数(淬火温度、淬火保温时间、回火温度和回火保温时间)对力学性能的影响,并确定了最优热处理工艺制度为930℃,40 min淬火和480℃,50 min回火.经最优热处理工艺处理后,其力学性能为:屈服强度895 MPa,抗拉强度1030MPa,伸长率15%,断面收缩率54%,冲击功53.3 J,满足了GB/T 17396—1998标准中对27SiMn钢的性能要求.     

基于集成算法的路段短时行驶时间预测

为了更好地解决路段行驶时间的短时预测问题,提出并改善了一种基于树的集成算法。针对小时间尺度下交通时变性强这一特性,构建更加鲁棒的梯度提升树（GBDT）以减少突变点的干扰。为了克服偏差 方差窘境,将随机树（RF）与GBDT进行融合,提出RF-GBDT的集成算法,并考虑各种历史旅行时间数据的相关变量以提高模型的可解释性。预测结果表明,与单独的RF或GBDT相比,RF-GBDT具有更好的预测准确度与算法稳定性。     

冷却速率对耐候钢高温力学性能的影响

铸坯高温力学性能是铸坯受力过程中决定裂纹产生的关键因素。在Gleeble热/力模拟实验机上模拟测试1.65, 5,10 ℃/s ３个冷却速率下耐候钢Q450NQR1铸坯高温力学性能。在1 050 ℃,3个冷却速率下试样都出现奥氏体动态再结晶;在850 ℃,塑性值都达到最低点,断面收缩率接近23%;随着冷却速率的增大,铸坯易产生裂纹的第III塑性区扩大,且向低温段扩展,塑性凹槽变深;增大冷却速率钢的各种强度降低,但增大到一定程度后强度不再降低;小的冷却速率下,奥氏体晶界上很少析出网状或薄膜状铁素体、微合金的     

Ti对高强耐候钢力学性能的影响

通过光学显微镜、透射电镜(TEM)以及力学性能测试等手段分析了薄板坯连铸连轧(TSCR)工艺生产Ti微合金化高强耐候钢的成分及工艺对显微组织和力学性能的影响.研究结果表明:钢中加入Ti,屈服强度有明显的提高;钛质量分数为0.05%～0.08%时,高强耐候钢的晶粒尺寸随着钛含量的增加基本不变;高强耐候钢强度的提高主要取决于钢中有效钛的含量,有效钛不仅与钛的含量有关,而且还与S, N的含量有关;在有效钛含量一定的条件下,析出强化的大小主要取决于轧后的卷取温度.