磨削烧伤声发射智能监测的研究进展

根据磨削加工中声发射监测的特点,在论述信号分析方法的基础上,重点综述了声发射技术在磨削烧伤智能监测中的研究进展;并揭示了声发射监测在砂轮钝化、工件表面粗糙度等领域的最新研究成果。最后阐述了基于声发射监测的智能磨削系统的发展现状,展望了智能声发射监测技术在磨削加工中的发展前景。     

基于LabVIEW开发平台的磨削加工声发射监测系统研究

磨削加工在线监测可降低磨削过程中工件碰撞及工件、砂轮破碎、影响磨削质量等损失，避免各种安全事故的发生，也可确定砂轮修整与更换的最佳时机，从而提高砂轮使用寿命和磨削质量。传统方法是利用功率、力、扭矩、振动、加速度等技术来监测磨削过程，但它们在灵敏度、响应速度等方面都存在着一定的局限，而利用声发射（Acoustic Emission，简称AE）技术监测磨削过程能够较好地解决上述问题。本文对磨削加工过程中产生声发射现象的机理进行了分析，利用虚拟仪器技术与声发射技术构建了基于虚拟仪器的磨削加工过程声发射监测系统，并进行了试验。试验表明，不同工况下，声发射信号随着主轴转速的提高、AE传感器安装位置与磨削区距离的缩小、磨削深度的增大以及砂轮与工件之间距离缩小而变得更加剧烈。     

工程陶瓷磨削声发射和磨削温度磨削力联合监测的研究

磨削声发射可以对磨削过程实时监测,磨削声发射信号均方根AERMS可以用来分析磨削过程的特征.为了使用磨削力、磨削温度、磨削声发射来联合对工程陶瓷氧化锆与氧化铝磨削过程进行全面监测和对工程陶瓷磨削机理进行深入研究,通过实验研究了磨削声发射信号与磨削力和磨削温度之间的内在联系,建立了工程陶瓷氧化锆与氧化铝磨削声发射信号与磨...     

声发射技术在磨削加工监测中的应用

近十几年来,声发射技术在机械加工方面获得了普遍的关注,其理论与方法已成为现代制造系统用来监测加工过程状态的一个重要的研究领域。文中概述了声发射（AE)技术的基本原理,讨论了磨削过程中声发射源产生机理,介绍了声发射技术在磨削加工监测方面的研究进展,简要指出了研究中存在的主要问题和发展方向。     

陶瓷材料高速深磨声发射信号分析

通过实验分析了工程陶瓷材料在高速深磨中不同磨削参数与声发射信号的关系。实验表明：材料、砂轮速度、工作台速度、切深4个因素与声发射信号有着很好的对应关系。声发射信号包含了大量有用的信息，可以利用声发射技术对陶瓷磨削过程进行有效的监测。     

磨削加工监测技术研究进展

针对磨削加工监测技术的研究现状,系统介绍了声发射监测、功率与电流监测、力和力矩监测、振动与位移监测、磨削温度监测以及集成监测等技术。分析了各种监测技术特点,并对磨削加工监测技术的发展方向进行了展望。     

光学玻璃磨削状态的声发射监测技术

本文通过建立光学玻璃磨削声发射状态监测系统,研究分析了光学玻璃超精密磨削过程中不同磨削工艺参数所对应声发射信号变化之间的关系.并通过该研究结果优化磨削工艺参数,使磨削后的光学玻璃表面粗糙度达到0.02μm,实验结果证明了声发射监测系统在光学玻璃超精密磨削过程中的实用性.     

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。     

声发射监控磨削过程中影响因素的实验研究

本文利用自制AE-I型声发射磨削检测仪进行试验，在分析磨削接触前后声发射信号变化的基础上，指出磨削接触前后声发射信号会出现很大变化，可以根据声发射信号的变化作为磨削接触的标志；在分析磨削过程中影响声发射信号大小的因素基础上，指出了影响声发射信号大小的主要因素，并提出了砂轮钝化的检测方法。     

Monitoring of through-feed centreless grinding processes with acoustic emission signals

Grinding is a mechanical removal process applied mainly in finishing operations of hardened workpieces to produce small tolerances with high-quality. Especially, centreless grinding is broadly used in serial production due to the requirement of high accuracy in process. Centreless grinding is used to produce several mechanical components such as, bushings, needles, ball bearings, valves, and stems for shock absorbers. However, the setup of machine tools is very complex and needs long time due to the great number of input variables that should be checked and configured. The acoustic emission monitoring can be used to help the first setup or during the grinding process becoming a on-line detection system. Considering the importance of obtaining an efficient methodology to predict and detect the surface quality and the dimensional errors, a monitoring of the frequency on the spectrum of acoustic emission (EA) was conducted, related to surface roughness Rz, cylindricity, and roundness. The FFT and Wavelet were applied aiming to help the analysis of data and provide the best understanding of the signal and generating an intelligent information in the automation in grinding process. Thus, in this work the results showed that the analysis of the harmonic content of acoustic emission signal is a powerful tool to monitoring the centreless grinding process.     

外圆磨削在线优化

基于基本的磨削模型,利用小生境蚁群算法和小生境粒子群算法,对磨削进给量和磨削深度进行在线优化。并采用Matlab编程,利用声发射信号和优化模型,对磨削过程进行仿真,实现了外圆磨削实际磨削过程的在线监测和优化。对提高磨削质量和磨削效率,实现磨削过程的在线监测和智能化控制有着极大的推动作用。     

齿轮磨削烧伤检测方法研究现状及发展方向

分析了齿轮磨削烧伤的危害,总结并比较了针对齿轮磨削烧伤的多种检测方法及特点。根据检测与烧伤发生的时间先后,分为事先预防和事后检测的方法。事先预防的方法包括临界常数法、磨削力比法、磨削温度监测法、神经网络预测磨削烧伤;事后检测的方法包括酸蚀法、表层显微硬度法、金相检测法、变质层深检测法等有损检测方法以及目测法、X射线衍射残余应力检测法、成分分析法、涡流检测法、CCD图像法、磁弹法、声发射在线监测等无损检测方法。针对每种方法的研究进展,讨论了各种方法适用的范围和局限性,并进一步指出齿轮磨削烧伤检测的发展方向。     

磨削粗糙度在线检测方法的实验研究

本文对基于摩擦声发射信号的磨削粗糙度在线检测方法进行了实验研究。实验结果表明，采用声发射传感器探头与磨削表面摩擦产生的声发射信号的特征可以对磨削表面粗糙度进行评价，建立了摩擦声发射信号特征与磨削表面粗糙度之间的对应关系，并通过实验对该方法的可行性进行了实测。结果表明，探针与工件表面摩擦声发射信号的FFT和RMS特征与磨削粗糙度有很好的对应关系，可用于磨削表面粗糙度的在线检测。     

磨削过程知识学习

磨削加工光磨时间、加工节拍直接体现磨削加工参数,粗磨过程的好坏是磨削过程的关键,磨削过程声发射信号粗磨段上升部分包含着磨削过程最丰富的信息,采用平均三角分配模糊规则对磨削过程声发射信号粗磨段上升部分进行知识获取和自学习,建立磨削加工光磨时间、加工节拍与磨削声发射曲线粗磨段上升部分斜率之间的对应关系,据此可得到任意光磨时间、加工节拍时对应的磨削声发射曲线粗磨段上升部分斜率。以此判断加工参数选择的合理性,以实现磨削加工的加工参数自动选择和智能控制,确保加工质量,实现磨削过程加工参数在线调整、磨削智能化。     

Grinding wheel wear monitoring based on wavelet analysis and support vector machine

A novel grinding wheel wear monitoring system based on discrete wavelet decomposition and support vector machine is proposed. The grinding signals are collected by an acoustic emission (AE) sensor. A preprocessing method is presented to identify the grinding period signals from raw AE signals. Root mean square and variance of each decomposition level are designated as the feature vector using discrete wavelet decomposition. Various grinding experiments were performed on a surface grinder to validate the proposed classification system. The results indicate that the proposed monitoring system could achieve a classification accuracy of 99.39% with a cut depth of 10?μm, and 100% with a cut depth of 20?μm. Finally, several factors that may affect the classification results were discussed as well.     

基于贝叶斯网络的平面磨削状态智能监测技术研究

为解决平面磨削过程中工件表面粗糙度预测和砂轮钝化监测困难的问题,利用贝叶斯网络建立了平面磨削状态智能监测模型。该模型在获取系统磨削用量和工件材料的基础上,在线提取磨削声发射信号的峭度系数,可以有效预测工件粗糙度和识别砂轮钝化状态,为数控系统调节加工参数提供参考。该模型在平面磨床的磨削监测试验中取得了良好的效果。     

Application of Hilbert–Huang Transform to acoustic emission signal for burn feature extraction in surface grinding process

This paper presents a sensor system using motor current sensors, voltage sensors, accelerator and acoustic emission sensor for grinding burn feature extraction. The new method, Hilbert–Huang transform (HHT), was applied as a signal processing tool to digest the raw acoustic emission and accelerator signals and to extract grinding burn features. A filtering criterion using average energy percentage of IMF components was proposed in order to simplify the calculation. Five IMF components were selected based on this criterion and their marginal spectra were calculated. The marginal spectral amplitude of the first three IMF components and the spectral centroid of the last two IMF components clearly reflected the occurrence of grinding burn. Results indicate that the application of HHT to acoustic emission signals in grinding burn detection is of great potential. Besides, the wheel rotation speed can be successfully uncovered through the intrinsic mode function (IMF), which verified the physical meaning of the EMD method.     

Intelligent diagnostic technique of machining state for grinding

Successful grinding of a final product depends upon a large number of parameters that affect the grinding result and are strongly interlinked. It is, therefore, difficult to detect directly the generation of grinding faults such as chatter vibration and burning. In this paper, to achieve the development of an intelligent diagnostic technique for chatter vibration and burning phenomena on grinding process, acoustic emission signals were processed and signal parameters of the acoustic emission were also determined. In addition, a neural network was used as a diagnostic technique of the grinding state. A momentum coefficient, learning rate, and structure of the hidden layer were determined during the iterative learning process and the performance of the diagnostic technique was evaluated.