Surface Nanocrystallization of Steel 20 Induced by Fast Multiple Rotation Rolling

In order to expand the application of steel 20 in precision device,fast multiple rotation rolling( FMRR) is applied to fabricate a nanostructured layer on the surface of steel 20. The FMRR samples are then Cr-Rare earth-boronized under low-temperature. The microstructure of the top surface layer is characterized by transmission electron microscopy( TEM). Microhardness of the top surface is measured by a Vickers microhardness tester. The boride layer is characterized by using scanning electron microscopy( SEM).Experimental results show that a nanostructured layer with their grain size range from 200 to 400 nm is obtained in the top surface layer. The microhardness of FMRR sample changes gradiently along the depth from about274 HV in the top surface layer to about 159 HV in the matrix,which is nearly 1.7 times harder than that of the original sample. The penetrating rate is enhanced significantly when the FMRR samples are Cr-Rare earthboronized at 600 ℃ for 6 h. Thickness of the boride layer increases to around 20 μm,which is nearly twice thicker than that of the original sample.

Effect of Surface Nanocrystallization Induced by Fast Multiple Rotation Rolling on Cr-Rare Earth-Boronizing for Steel 45 under Low-Temperature

In this paper, fast multiple rotation rolling （FMRR） is applied to fabricate a nanostructured layer on the surface of steel 45. The FMRR samples are then Cr-Rare earth-boronized under low-temperature. The boride layer is characterized by using Scanning electron microscopy （SEM） and X-ray diffraction （XRD）. Experimental results indicate that the thickness of the boride layer is greatly increased by surface nanocrystallization. The boride layer with relatively continuous structure instead of the zigzag teeth structure is obtained, and the penetrating rate is enhanced by 2. 5-3.7 times when the FMRR samples are Cr-Rare earth- boronized at the temperature of 570 %, 600℃ and 650℃ for 6 h. The boride layer fabricated on the FMRR sample consists of single phase Fe2B. Severe plastic deformation with the grain size of approximately 100 nm in the top surface layer of steel 45 is observed, and the thickness of the plastic deformation layer is about 30 6xm. The microstructure in the top surface layer is characterized by Transmission electron microscopy （TEM）. Grain boundaries are largely increased with high stacking fault energy after FMRR, leading to a significant enhancement of RE boron-chromizing speed.

The development and application of ultra fast cooling in hot-rolled

Ultra fast cooling is a new technology which used to control the hot-rolling strip cooling in recent years on the international developed.If suitably cooperated with a number of other new controlled rolling technologies,can achieve fast and accurate temperature control in the hot-rolled strip production process to obtain corresponding transformation microstructure and ideal mechanical properties.This article describes the technical principle and layout of ultra fast cooling in hot-rolled as well as application profiles in the major iron and steel enterprise in China and abroad.Carried out the layout of ultra fast cooling analysis on the adaptability of steel that install between the finishing mill and laminar cooling,on this basis,proposed the use of ultra fast cooling technology proposals.

CEEMD与FastICA结合的故障特征提取方法

针对滚动轴承故障特征信息难以分离的问题,提出了互补式集成经验模态分解(Complementary Ensemble Empirical Mode Decomposition,CEEMD)与快速独立分量分析(Fast Independent Component Analysis,Fast ICA)结合的故障特征提取方法。首先将振动信号进行CEEMD分析,分解成不同尺度的固有模态分量(Intrinsic mode function,IMF);然后通过敏感度评估算法对分解信号进行重组,并利用Fast ICA对其进行降噪处理;最后对Fast ICA分离的信号进行Hilbert包络谱分析,获取故障特征信息。将此方法应用于滚动轴承振动信号故障分析,实验证明了所提方法的有效性。

Controlled Rolling and Cooling Process for Low Carbon Cold Forging Steel

Effect of controlled rolling and cooling process on the mechanical properties of low carbon cold forging steel was investigated for different processing parameters of a laboratory hot rolling mill. The results show that the specimens with fast cooling after hot rolling exhibit very good mechanical properties, and the improvement of the mechanical properties can be attributed mainly to the ferrite-grain refinement. The mechanical properties increase with decreasing final cooling temperature within the range from 670 ℃ to 570 ℃ due to the finer interlamellar spacing of pearlite colony. The specimen with fast cooling after low temperature rolling shows the highest values of the mechanical properties. The effect of the ferrite grain size on the mechanical properties was greater than that of pearlite morphology in the present study. The mechanical properties of specimens by controlled rolling and cooling process without thermal treatment were greatly superior to that of the same specimens by the conventional rolling, and their tensile strength reached 490 MPa grade even in the case of low temperature rolling without controlled rolling. It might be expected to realize the substitution medium-carbon by low-carbon for 490 MPa grade cold forging steel with controlled rolling and cooling process.

基于VMD与FastICA的滚动轴承故障诊断

针对滚动轴承故障成分易被强背景噪声淹没,造成故障特征提取困难的问题,提出了一种采用变分模态分解(variational mode decomposition, VMD)与基于负熵的快速独立成分分析(fast independent component analysis, FastICA)相结合的故障诊断方法。利用经验模态分解(empirical mode decomposition, EMD)确定VMD模态数n,采用VMD将目标信号分解为n个模态分量;以连续的3个分量为一序列组合进行FastICA分析,从每组结果中选取一个最优分量,共n-2个;重构故障信号并进行Hilbert包络谱分析。通过试验分析并与EMD-FastICA方法、单一VMD方法比较,结果表明该方法能够更加清晰准确地提取故障特征信息,可用于轴承故障诊断。

基于LMD和FastICA的轴承故障诊断研究

针对恶劣环境影响下滚动轴承微弱故障特征难以提取的问题,提出一种基于局部均值分解(LMD)和快速独立分量分析(FastICA)算法相结合的新方法。首先,以快速谱峭度选取合适的滤波参数进行滤波;然后,利用LMD方法对滤波处理后的目标信号进行局部均值分解得到多个乘积函数(PF),并根据筛选准则选取合适的分量进行信号重构;最后,使用FastICA算法实现源信号和噪声的分离,有效地提取故障特征。

基于LMS和Fast-Kurtogram的滚动轴承早期故障诊断

针对滚动轴承早期故障特征提取困难的问题,提出一种LMS(Least Mean Square,LMS)算法降噪、FastKurtogram选频和共振解调技术相结合的滚动轴承故障诊断方法。首先对采集到的信号进行自适应降噪,减弱背景噪声的影响;然后利用谱峭度值对故障信号中瞬态成分敏感的特性,通过计算降噪后信号的快速峭度图,确定滤波器最优频带中心和带宽;最后进行共振包络解调提取出滚动轴承早期故障特征。通过仿真和实验验证分析,验证了该方法在滚动轴承早期故障诊断中的适用性和有效性。

Application of a new feature extraction and optimization method to surface defect recognition of cold rolled strips

Considering that the surface defects of cold rolled strips are hard to be recognized by human eyes under high-speed circumstances, an automatic recognition technique was discussed. Spectrum images of defects can be got by fast Fourier transform （FFF） and sum of valid pixels （SVP）, and its optimized center region, which concentrates nearly all energies, are extracted as an original feature set. Using genetic algorithm to optimize the feature set, an optimized feature set with 51 features can be achieved. Using the optimized feature set as an input vector of neural networks, the recognition effects of LVQ neural networks have been studied. Experiment results show that the new method can get a higher classification rate and can settle the automatic recognition problem of surface defects on cold rolled strips ideally.

基于OFMD和FSC的滚动轴承复合故障诊断

针对滚动轴承的复合故障诊断问题,深入研究了一种基于优化特征模态分解和快速谱相关的复合故障诊断方法。首先,通过理论分析,提出脉冲能量因子指标来实现特征模态分解的参数选择以及最优分量的选取;然后,基于快速谱相关原理设计谱相关相对强度曲线和改进快速谱相关图,用于确定不同故障调制后对应的最优载波,对最优载波进行包络处理,从而分离轴承的复合故障特征,最终实现复合故障的准确性诊断。通过模拟故障试验和工程案例分析结果表明,该文所提方法相比于经验模态分解能够有效滤除噪声干扰,具有良好的鲁棒性,同时,避免了解卷积方法设定参数的缺陷,且与Autogram方法相比,能够有效分离复合故障特征,避免复合故障特征成分耦合。

基于多算法融合的滚动轴承早期微弱故障诊断

针对滚动轴承故障初期故障信号微弱,故障特征难以识别问题,提出了一种基于多算法融合的滚动轴承早期微弱故障诊断的方法。首先,利用多小波自适应阈值法对轴承故障信号进行降噪处理;其次,利用快速谱峭度对消噪后的信号提取谱峭度最大的轴承冲击信号;接着,对剩余信号进行集合经验模态分解(EEMD)并基于峭度准则筛选前3个峭度最大的本征模态分量(IMF)同轴承冲击信号进行信号叠加,得到重构信号。最后,对重构信号进行快速谱相关,增强周期性成分,实现轴承早期微弱故障诊断。仿真实验表明:多算法融合的滚动轴承早期微弱故障诊断的方法可有效消弱噪声成分的干扰,增强微弱故障特征,实现滚动轴承早期微弱故障诊断。

快速铸轧中的接触热导及带坯在铸轧区的温度分布的仿真分析

研究了快速铸轧辊套与铸坯之间具有强温变特性耦合关系的接触热阻的计算方法，采用ＡＤＩ差分方法和ＴＤＭＡ算法对二维传热模型进行了数值模拟。

经验快速谱峭度及其在滚动轴承故障诊断中的应用

快速谱峭度(Fast Kurtogram,FK)通过构造有限冲击响应滤波器从频谱上将信号二分或三分为几个不同频带的分量后,判断每个分量的谱峭度大小以提取调制信息。该方法运算速度很快,但有时包含故障信息的频段无法被均分的谱峭度图容纳,甚至可能导致提取出的分量中无法检测到明显的故障信息。提出一种新的频谱边界划分方法用以优化快速谱峭度,并称之为经验快速谱峭度(Empirical Fast Kurtogram,EFK)。首先,将信号频谱的傅里叶变换函数中代表频谱趋势的成分提取出来,并搜索其极小值点序列;然后,以极小值点在频谱中的位置作为频谱划分的边界,采用Meyer小波构造滤波器并重构信号分量以求取峭度;最终,构造出一种新的快速谱峭度图,选择谱峭度最大的频段提取故障信息。该方法依据信号频谱的趋势划分边界可以有效地避免由于均分频谱导致的不合理现象,模拟信号及滚动轴承内圈、外圈故障信号证明了该方法的有效性。

热轧超快速冷却装置的开发现状与设计分析

本文简要介绍了热轧超快冷技术的基本原理和发展现状,重点介绍了超快冷装置开发的难点及关键点,并提出了相应的解决方法及设计思路,为超快冷装置的开发提供了参考。

基于RHP-IVFSA的多智能体编组任务分配动态优化

针对多作战智能体编组协同作战中任务分配的动态优化问题,提出一种基于滚动时域策略的多编组任务分配动态优化方法。以任务执行效率为目标函数,建立了满足个体任务时窗和编组资源损耗约束的问题模型。给出与突发事件特征对应的预测窗口、滚动窗口和滚动驱动机制。设计了一种改进快速模拟退火对优化子问题予以求解,给出解方案表达、邻域解生成、冲突消解等步骤,采用高温随机贪婪搜索、回火技术、禁忌设计和精英保留策略,避免算法陷入局部最优,提高算法的计算效率。案例的仿真计算表明,所建模型和求解方法可以对多编组任务分配计划进行在线优化,并使任务的执行效率始终维持较高水平。

典型快速谱峭图算法的研究及应用

针对工程实际中状态监测下的具体机械部件在特定运行环境中发生故障的原因及类型具有典型性,在分析快速谱峭图算法的基础上,提出了典型快速谱峭图算法。该算法借鉴快速谱峭图算法分层和选取中心频率的思想,以典型故障的三倍特征频率为带宽,快速定位谱峭度最大的频率区间,并将该算法应用于共振解调技术滤波器参数的确定。为了验证该算法的有效性,在滚动轴承故障诊断试验台上进行了振动测试,采用基于典型快速谱峭图算法的共振解调技术进行故障诊断和类型识别。结果表明,基于典型快速谱峭图算法的共振解调技术能够较好的诊断典型故障,具有一定的工程应用价值。

板材轧制过程中快速有限元在线算法

有限元法(Finite element method,FEM)在轧制过程力能参数、变形参数、温度参数以及组织演变等模拟与预测中发挥了重要作用,但由于有限元求解问题时所需的计算时间较长而无法实现轧制过程参数的在线计算。快速有限元(Fast finite element,FFE)方法具有计算精度高和计算速度快等优点。基于刚塑性有限元(Rigid plasticity finite element method,RPFEM)方法开发了板材轧制过程FFE程序。讨论了单元数目对计算时间和计算精度的影响规律,研究加快计算收敛速度和减少搜索时间的在线算法,包括采用阻尼Newton-Brunt法相结合减少一维搜索时间;采用改进循环中点求积Newton法为Newton-Raphson迭代法寻求比较好的初始值;采用信赖域Newton混合迭代法以及改进的Cholesky分解使Hessian矩阵强迫正定以加快收敛速度;研究计算机操作系统、软硬件条件对计算时间的影响。开发的FFE在线设定模块在某热轧带钢厂取得良好的应用效果。

热带钢超快速冷却条件下的对流换热系数研究

建立了热带钢超快速冷却过程的导热微分方程,采用有限差分方法计算了薄带钢实现超快速冷却(对于4 mm以下的薄带钢,冷却速率可达300℃/s)所需的带钢表面对流换热系数。同时,在实验室条件下采用厚度为20 mm的钢板进行了超快速冷却试验,得到了超快速冷却条件下的带钢表面对流换热系数与冷却水流量的关系。结果表明,在一定水流量范围内随着冷却水量的增加,带钢表面换热系数逐渐增加;采用所确定的换热系数对不同厚度钢板得到的温降曲线与实测值吻合较好,具有较高的精度。

控制冷却在板带材开发生产中的应用

概述了快速冷却及直接淬火在提高板带材性能及钢种开发方面的发展应用情况,并介绍了热轧带钢的新型控制冷却技术———超快速冷却(UFC)。目前,UFC的冷却速率已超过300℃/s。UFC技术能明显提高带钢的强度和韧性、改善材质性能。还描述了UFC装置的应用实例及在开发超级钢、多相高强钢、相变诱导塑性钢等高附加值钢铁材料方面的应用潜力。随着先进钢铁材料开发的需要,新的控制冷却技术已由传统的对轧后开冷、终冷温度的控制向基于连续冷却转变CCT曲线的相变要求而对板带整个冷却过程的微观组织相变控制方向发展。

基于VMD和谱峭度的滚动轴承早期故障诊断方法

针对滚动轴承故障信号为多分量非平稳振动信号、故障早期特征微弱诊断困难的问题,该文提出变分模态分解(VMD)结合谱峭度的滚动轴承早期故障诊断方法。首先对振动信号进行VMD分解得到若干分量信号,选择峭度最大分量作为最优分量,然后对最优分量进行快速谱峭度计算并进行带通滤波、凸显故障冲击成分,通过分析滤波信号包络谱中故障频率成分实现故障诊断。实验数据分析结果表明该方法能有效诊断轴承早期故障,有一定的工程应用价值。