固体声故障诊断系统研制

本文介绍我们最新研制的具有国内先进水平的固体声故障诊断系统，该系统利用所测得的机器内部固体声信息进行信号特征分析、系统识别及工况监测。经Ｃ６２０－１型车床实验论证，该系统识别正确率可达到９８％至１００％，值得推广应用。     

磨削过程计算机集成智能监控系统

利用神经网络建立了磨削过程计算机集成智能监控系统,该系统将磨削过程监测、故障诊断和反馈控制组成一个有机整体.通过在线提取磨削声发射(AE)信号RMS峰值、FFT峰值、信号标准偏差以及信号累积幅值增量,可以实现磨削烧伤、磨削颤振、砂轮钝化等故障的在线实时诊断.通过反馈控制系统实现磨削参数的实时调整,提高了磨削加工的性能、效率和磨削质量的稳定性.实测结果证明了该系统的有效性.     

有效流程监控可保证内圆磨削质量

流程监控有不同的工作准则来适用于切削加工工艺。对于硬度精加工,固体声发射是有效的,因为特殊的干扰量,如较高的主轴转数和冷却润滑材料的高压是可以控制的。利用不同的传感器和处理器可以调节、监控并完善磨削过程。     

磨削过程监控及工艺优化技术

为提高磨削零件表面质量,针对磨削过程的多因素影响,利用现代各种传感器信号来监控磨削加工过程的状态信息,分析研究各磨削阶段加工质量与不同监控信号的特征关系,并根据监控信号特征对整个磨削过程进行了工艺优化.通过对轴承套圈磨削的大量试验研究,验证了该技术具有很强的实用性和通用性.     

基于LabVIEW的数控中小孔内圆磨床监控系统软件设计

为提升数控内圆磨床磨削效率及对磨削加工动态监测,提出了构建基于LabVIEW平台的数控内圆磨床监控系统。监控系统通过AE传感器采集信号,利用LabVIEW平台编制控制软件实现信号的处理分析及状态决策,并通过MODBUS通讯协议将决策信息实时反馈给机床PLC,进而通过PLC控制机床实现磨削效率的提升与磨削过程控制。     

基于声发射信号的精密外圆切入磨削时间评估算法及试验研究

针对精密外圆切入磨削智能监控的需求,设计一种基于声发射信号的磨削时间在线评估方法。通过建立声发射信号方均根值曲线预测模型,获得声发射信号与磨削系统时间常数的关系,设计磨削系统时间常数在线计算方法;利用在线检测的声发射信号识别砂轮运动去除状态,推导基于声发射信号的外圆切入磨削表面粗糙度评价和工件几何精度预测模型,以此建立砂轮进给与驻留时间的评估算法;编写磨削时间分析评估软件,设计磨削时间在线评估方法,通过加工试验分析磨削时间对磨削加工精度与表面粗糙度的影响规律,并对评估算法进行验证。试验结果表明:该评估方法能够根据磨削时间有效评价加工质量,为精密外圆切入磨削智能监控与工艺优化提供决策依据。     

基于声发射信号的外圆切入磨削去除率监测

针对精密外圆切入磨削加工的在线监测需求,提出一种采用声发射信号实现轴类零件材料去除率在线监测的方法。根据声发射信号强度与磨削力之间的联系,建立了声发射信号均方根曲线的预测模型,利用该预测模型研究了砂轮进给阶段和驻留阶段磨削系统时间常数的理论计算方法,推导了声发射信号均方根曲线与工件材料去除率的关系;编写了在线监测软件,利用声发射传感器实现了精密外圆切入磨削的材料去除率预测。实验证明,所建立的声发射信号均方根曲线模型具有良好的预测精度,基于该模型能够实现磨削系统时间常数在线评估,并实现精密轴类零件材料去除率的实时在线监测。     

基于Shannon熵与声发射信号的CBN砂轮性能监测方法研究

为了在磨削加工过程中能够有效判别CBN(Cubic Boron Nitride)砂轮的磨削性能,提出了一种基于Shannon熵理论与声发射信号的CBN砂轮性能监测方法。首先,利用声发射传感器采集CBN砂轮磨削加工过程中的声发射信号,基于最大信息熵对CBN砂轮磨削加工过程中的声发射信号进行概率密度估计,获得磨削加工过程中声发射信号的最大熵概率密度分布。然后,通过分析研究CBN砂轮在修整过后循环磨削以及不同直径剩余磨削时的声发射信号特征,根据交叉熵原理分析CBN砂轮不同磨削性能时声发射信号最大熵概率密度分布,并通过设定交叉熵阈值来辨别磨削加工过程中CBN砂轮的磨削性能。最后,为验证该方法的实用性,在某工厂CBN砂轮磨削产品生产线上进行大量实验研究,结果表明,该方法对CBN砂轮磨损状态及CBN砂轮剩余寿命进行有效监测,验证了该方法监测CBN砂轮在磨削加工过程中磨削性能的有效性。     

基于BP神经网络的轴承套圈沟道磨削粗糙度识别

针对轴承套圈沟道磨削监测方法间接、监控信号偏少、识别效率不高、准确率偏低等不足,采用相关分析初选8个与粗糙度相关性较高的信号特征,再采用主成分分析,根据主成分贡献率以及累积贡献率,进一步将8个信号特征转化为3个主成分,采用BP神经网络建立主成分与沟道磨削粗糙度之间的映射关系模型,利用Matlab软件进行训练和验证,粗糙度识别正确率超过95%,能够提高轴承套圈沟道磨削过程的质量监控能力。     

声发射(AE)信号在砂轮钝化程度检测中的应用

本文利用声发射（AE）信号的归原处理法，在线监测小批量、多品种磨削过程砂轮钝化程度，利用该方法可以克服仅靠监测AE信号幅值变化不能监测工件材料、加工要求和磨削参数经常变化环境下砂轮钝化程度的缺陷；实验结果表明，声发射（AE）信号的归原处理法能够有效监测砂轮的钝化。     

结构噪声源识别的能量流分析方法

基于能量流理论提出了识别结构噪声源的分析方法。推导了利用两点线性加速度传感器测量能量流的互谱表达式。对测量过程中出现的主要误差进行了分析,提出了相应的减少误差的措施。通过试验验证了测量方法的精度及识别结构噪声的主要过程。     

Contact length estimation in grinding using thermocouple measurement and numerical simulation

Important research efforts are being devoted to the analysis and optimization of grinding operations. Due to the large number of variables involved in the process, a deeper understanding of the nature of contact mechanics between wheel and workpiece is required. The possibility of a sound estimation of the actual contact length is a critical issue, since this is related to thermal phenomena, chip geometry and pressure distribution. In this work, a new methodology for the estimation of the actual contact length in grinding is presented. The proposal uses the theoretical definition using numerical simulation of an indicator that univocally determines accurately the contact time on the plot temperature vs. time as measured by contact thermocouples. Experimental results show an excellent fit with previous estimations of contact length found in literature. The method can be extended to the characterization of wheel topography.     

An acoustooptic method for sound velocity measurements in strongly sound-absorbing materials

An acoustooptic method for determining the velocities of longitudinal ultrasonic waves in a 10⁵ to 10⁸-Hz range in strongly absorbing (scattering) materials, when methods based on the sound transmission through a sample are unfit, is described. This method employs the acoustooptic determination of the acoustic impedance of the studied sample by measuring the reflection coefficient from the boundary between two contacting substances, namely, a liquid with the known (or measured) acoustic impedance and the studied solid sample. The method features the following advantages: (i) it is acceptable, when standard methods do not work due to the sound attenuation (scattering); (ii) there is no need for exact knowledge of sample sizes, the operating frequency, and the electromechanical coupling factor, and also for using reference samples; and (iii) the sound attenuation in a liquid and the distance from the sample surface to measurement points (required for calculations of the sound velocity) are easily measured. In this case, the velocity measurement error is usually 5–10%.     

基于ASOS-ELM的湿式球磨机负荷软测量方法

针对湿式球磨机在磨矿过程中内部负荷靠专家经验难以准确预测的问题,提出一种基于改进的共生生物搜索(ameliorated symbiotic organisms search,简称ASOS)-极限学习机(extreme learning machine,简称ELM)的磨机负荷软测量方法。首先,利用ELM算法建立磨机负荷软测量模型,运用ASOS算法优化软测量模型的隐含层参数;其次,以筒体振动与振声信号的特征信息构建磨机负荷特征向量,并将其作为软测量模型的输入,将磨机负荷参数作为输出;最后,通过磨矿负荷检测实验和对比分析表明,磨机负荷软测量模型的负荷参数预测准确率较高,泛化能力较强,为磨机磨矿效率的提高及控制优化提供了有益的指导。     

DEVELOPMENT OF AN OPTICAL SCANNER TO STUDY WEAR ON THE WORKING SURFACE OF GRINDING WHEELS

The efficiency of the grinding operation is highly dependent on the grinding wheel surface topography. Several methods for the evaluation of the grinding wheel surface have been developed in the last few years. In some of these methods, the grinding wheel has to be removed from the machine for an evaluation using a microscope or a profilometer. Some other methods are able to measure the topography at the grinding machine, but they do not give detailed information about grain distribution and wear. This paper shows a new method to map the grinding wheel surface. The proposed system is based on an optical scanner capable of measuring the light beam reflected from the wear flat areas on the abrasive grains over the whole wheel peripheral surface. The Mapping System for Grinding Wheels (MSGW) is able to acquire data with the wheel running at the work speed (30 m/s)and the measurement carried out on the grinding machine without stopping. The system is applied on a surface grinding operation where an Alj0₃grinding wheel is used. The results show that the system can be used to map the wear flats at the grinding wheel surface and also to analyze the wear phenomena efficiently.     

磁头研磨工序时间改进研究

在磁头的制造过程中,磁头研磨工序所花时间较长,为瓶颈工序。基于对工序现状的调查分析,利用研磨理论和工业工程的工作研究理论,从多方面对该工序时间进行了改善。通过对研磨余量、粗磨与精磨余量的分配以及润滑方式的优化,大幅度缩短了工序基本工作时间,通过作业评定与动作分析,利用ECRS原则,改善了工序辅助时间。改善后,工序时间降低了37.7%,基本实现了生产线平衡,使工序成本降低了4.7%。     

大导程滚珠丝杠副螺母的截形计算与加工仿真

根据螺旋面加工原理、齿形啮合原理及回转面砂轮加工螺母工艺建立了相应的数学模型,并推导出砂轮截形的计算公式;采用数值计算方法求解砂轮截形参数,进一步得到修整器靠模板的参数;在此基础上,利用Pro/E软件对大导程滚珠丝杠螺母、砂轮和砂轮修整器进行了实体建模,并对砂轮的修整过程和螺母的加工过程进行了运动仿真,有效地验证了砂轮截形的计算结果。     

整体硬质合金球头铣刀螺旋槽建模技术研究

球头铣刀外形复杂,工序繁多,在曲面加工中应用广泛。对整体硬质合金球头铣刀刃线和螺旋槽进行数学建模,采用等导程刃线模型生成S型球头刃线,解决球面刃线与柱面刃线过渡不光滑问题。采用蝶形砂轮刃磨螺旋槽,求解砂轮刃磨位姿。利用MATLAB对球头铣刀刃线模型进行仿真验证,计算砂轮位姿变换过程,并基于MATLAB软件中GUI模块设计砂轮磨削工艺系统。     

基于EMD复杂度与鉴别信息的磨削颤振预测

为避免磨削加工中出现颤振,提出一种基于经验模式分解(empiracal mode decomposition,简称EMD)复杂度与鉴别信息的颤振预测方法。采用经验模式分解对磨削加工中滤波后的振动信号进行分解,获得振动信号的本征特征函数;采用L-Z复杂度指标对本征特征函数进行复杂度分析,获得磨削颤振特征值;采用鉴别信息对复杂度变化进行量化,通过鉴别信息对磨削加工颤振进行预测。在外圆磨床实验平台对该方法的有效性进行了验证,分别采用变工件转速、变砂轮转速和变磨削厚度3种加工方式逼近颤振状态。分析结果表明,当磨削加工趋于颤振时对应的鉴别信息值增大。实验结果通过鉴别信息的大小可以对磨削加工中的颤振进行预测。     

A new digital measurement method for accurate curve grinding process

In this paper, an online measurement and error compensation system for curve grinding based on pattern recognition was presented and verified by experiments. The measurement system organization and its principle of operation were introduced in detail. The work piece and grinding wheel image were sampled at certain positions to avoid spark influence. In order to increase system resolution, images were sampled only at local areas of the work piece and grinding wheel. A discrimination technology based on a circular tolerance zone was proposed which can solve the problem of local image edge comparison. For image de-noising, a local threshold algorithm was applied to determine new wavelet coefficients. Furthermore, a two-step edge detection method was used to realize sub-pixel precision. Finally, a series of experiments were carried out to examine the detection precision of the image measurement system and its influencing factors. From experiments, it can be said that the proposed method in this paper is effective, and its detection precision is much better than traditional methods.