声发射监测技术在磨削加工中的应用

在磨削加工中，砂轮的工作状况是不断变化的，因此，对砂轮的修整及磨削过程进行监测是非常必要的。本文将声发射信号应用到对磨削过程的监测上，通过监测砂轮修整过程可以得到一致的砂轮表面质量。通过监测ＡＥ信号的幅值和频谱特征即可确定砂轮的寿命。用ＡＥ信号可以成功地检测出砂轮和工件的初始接触时间，为砂轮向工件进给时的准确对刀及高精度表面质量的获得提供了有效的方法。     

声发射监测技术在磨削加工中的应用

在磨削加工中,砂轮的工作状况是不断变化的,因此,对砂轮的修整及磨削过程进行监测是非常必要的。本文将声发射(AE)信号应用到对磨削过程的监测上,通过监测砂轮修整过程可以得到一致的砂轮表面质量。通过监测AE信号的幅值和频谱特征即可确定砂轮的寿命。用AE信号可以成功地检测出砂轮和工件的初始接触时间,为砂轮向工件进给时的准确对刀及高精度表面质量的获得提供了有效的方法。     

声发射技术在磨削及其研究中的应用

本文综述了声发射技术在磨削及其研究中的应用情况,对声发射技术在磨削质量的在线检测、磨削过程监视及砂轮参数的测定等方面的应用进行了具体研究探讨。     

磨削加工中声发射信号特性分析及其在烧伤预报中应用研究

对难加工材料进行磨削时,磨削表层产生的微观破坏和缺陷如磨削烧伤和裂纹等是最常见的问题。本文对高温合金、表面硬化钢等材料进行试验研究,采用声发射技术,并且运用自回归时序模型对信号进行分析。结果表明上件表面磨削状态的变化对模型参数和残差方差影响很大。本文根据模型的残差方差变化实现对磨削烧伤的在线监测和预报。     

基于声发射技术的砂轮磨损实验研究

砂轮磨损状态复杂多变,磨损信号干扰多,特征提取困难。文章针对砂轮磨损过程,提出一种基于声发射技术的砂轮磨损表征方法。利用小波能量分析对磨削过程声发射RMS(均方根)信号进行重构与消噪,研究声发射RMS(均方根)信号频谱矩心这一特征值与工件表面粗糙度的对应关系。得出砂轮修整初期频谱矩心低,砂轮磨损后频谱矩心显著增大,由于磨粒自锐作用,频谱矩心会呈现周期性变化规律;在同一周期内,处于低频段的砂轮磨削出的工件表面粗糙度必优于处于高频段的砂轮;在不同周期内砂轮磨削出的工件表面粗糙度不具有可比性;表明了磨粒自锐的随机性。而且随着砂轮磨损的增加,频谱矩心高频段持续时间越来越长,直至砂轮剧烈磨损。     

磨削加工中声发射信号特性分极及其在烧伤预报中应用研究

对难加工材料进行磨削时，磨削表层产生的微观破坏和缺陷如磨削烧伤和裂纹等是最常见的问题。本文对高温合金，表面硬化钢等材料进行试验研究，采用声发射技术，并且运用自回归时序模型对信号进行分析。结果表明工件表面磨削状态的变化对模型参数和残差方差影响很大。本文根据模型的残差方差变化实现对磨削烧伤的在线监测和预测。     

磨削过程中砂轮耐用度的试验研究

当砂轮磨钝时,磨削力、力比、磨削振动振幅、磨削噪声声压、磨削表面粗糙度以及工件不圆度均会发生急剧变化,因此可把它发生急变以前的某一时间作为砂轮的耐用度。本文根据所建立的砂轮耐用度判定标准,研究了外圆磨削300M超高强度钢时磨削参数对砂轮耐用度的影响。     

轴对称非球面轨迹包络磨削中砂轮对刀误差分析

硬脆材料的轴对称非球面光学零件所具有的回转曲面,既需要达到很低的表面粗糙度值又要有很高的几何形状精度,采用轨迹包络磨削加工是达到其精度要求的重要工艺方法。轨迹包络磨削过程中,砂轮的对刀误差是影响轴对称非球面形状精度的关键因素之一。通过建立砂轮的对刀误差数学模型,推导出轨迹包络磨削轴对称回转非球面过程中出现对刀误差时的磨削轨迹曲线方程,并分析了x方向存在、y方向存在、x和y方向同时存在对刀误差对轴对称回转非球面的曲面形状精度的影响。分析结果表明:砂轮的对刀误差将影响轴对称回转非球面的母线的形状和位置。     

Characterization of the grinding process by acoustic emission

The properties of a ground surface can be estimated on-line during manufacturing based on the analysis of acoustic signals emitted by the grinding process. This possibility is demonstrated using an experimental system comprising an external grinding machine, a data acquisition unit and an artificial neural network. In the initial phase of system application, an empirical model of the grinding process is formed in the memory of the neural network by self-organized learning driven by empirical data consisting of the acoustic emission spectrum and a surface roughness correlation function. After learning, the system applies the model to estimate the correlation function of the surface profile from the input acoustic emission spectrum. For this purpose, non-parametric regression, based on the conditional average estimator, is utilized. Experiments were done on the grinding of hardened steel workpieces by a corundum wheel. During formation of the model, the surface profile and its correlation function were determined off-line, while in testing system performance the surface correlation function was estimated on-line from the acoustic emission spectrum. With respect to the estimation error, three characteristic periods of the process were observed corresponding to grinding with a newly dressed, slightly worn, and worn out wheel. The best estimation is obtained during grinding by a slightly worn wheel.     

Trouble diagnosis of the grinding process by using acoustic emission signals

The focus of this study is the development of a credible diagnosis system for the grinding process. The acoustic emission signals generated during machining were analyzed to determine the relationship between grinding-related troubles and characteristics of changes in signals. Furthermore, a neural network, which has excellent ability in pattern classification, was applied to the diagnosis system. The neural network was optimized with a momentum coefficient (m), a learning rate (a), and a structure of the hidden layer in the iterative learning process. The success rates of trouble recognition were verified.     

The application of acoustic emission for precision drilling process monitoring

Precision drilling is a process where a close tolerance hole can be produced with a special drill bit without subsequent reaming. Producing a hole without reaming results in less overall processing time during hole preparation. Precision drilling is best accomplished by a robot with a computer controlled drilling end effector due to the high degree of process control required. Some aspects of the process, such as spindle speed, feed rate, and peck cycles, can easily be controlled by a computer controlled end effector. Other variables, such as drill bit wear, chipping, and point geometry variation, cannot be controlled with the end effector. These variables affect the diameter of the hole but cannot be detected unless the hole or the drill bit is manually inspected. It is not practical to stop the process and check the diameter after every hole. Therefore, a means to perform real time drilling process monitoring is required to detect if an oversized hole is being drilled. The primary objective of this research was to correlate the diameter of a hole drilled in steel with any acoustic emission (AE) signal measurement parameter. The secondary objective was to correlate drill bit lip height variation, which has a significant influence on the diameter of a hole, with any AE signal measurement parameter. The results of this study showed that acoustic emission could only be correlated to hole diameter variations if those variations were related to the lip height variations. However, AE energy and RMS were correlated to lip height variations under a wide variety of conditions.     

The effects of cutting fluid application methods on the grinding process

It is well known that a boundary layer of air is entrained around a rotating grinding wheel. The effects of the boundary layer have been under some scrutiny in recent years with most research being based on trying to overcome the boundary layer. The current investigation aims to show through experiment and modelling, the effects of the boundary layer on cutting fluid application and how it can be used to aid delivery by increasing flow rate beneath the wheel. Results from three experiments with different quantities of cutting fluid passing through the grinding zone are presented.     

Neural network detection of grinding burn from acoustic emission

An artificial neural network (ANN) approach is proposed for the detection of workpiece “burn”, the undesirable change in metallurgical properties of the material produced by overly aggressive or otherwise inappropriate grinding. The grinding acoustic emission (AE) signals for 52100 bearing steel were collected and digested to extract feature vectors that appear to be suitable for ANN processing. Two feature vectors are represented: one concerning band power, kurtosis and skew; and the other autoregressive (AR) coefficients. The result (burn or no-burn) of the signals was identified on the basis of hardness and profile tests after grinding. The trained neural network works remarkably well for burn detection. Other signal-processing approaches are also discussed, and among them the constant false-alarm rate (CFAR) power law and the mean-value deviance (MVD) prove useful.     

Tool monitoring of small drills with acoustic emission

The risk of fracture is higher in the case of small-diameter drills than with any other cutting tool. Analysis of cutting forces or motor current is fundamentally unsuitable for practical applications, owing to the low forces or low sensitivity involved. Suitably-located AE sensors can, however, be used to obtain signals which allow tool wear and fracture to be monitored. The monitoring system realized in the study allows the tool-life dispersions of the drills, which are generally in a ratio higher than 1:3, to be exploited. Tool fractures due to high wear can also be avoided.     

Precision manufacturing process monitoring with acoustic emission

Current demands in high-technology industries such as semiconductor, optics, MEMS, etc. have predicated the need for manufacturing processes that can fabricate increasingly smaller features reliably at very high tolerances. In situ monitoring systems that can be used to characterize, control, and improve the fabrication of these smaller features are therefore needed to meet increasing demands in precision and quality. This paper discusses the unique requirements of monitoring of precision manufacturing processes, and the suitability of acoustic emission (AE) as a monitoring technique at the precision scale. Details are then given on the use of AE sensor technology in the monitoring of precision manufacturing processes; grinding, chemical–mechanical planarization (CMP) and ultraprecision diamond turning in particular.     

Investigation of acoustic emission signals under a simulative environment of grinding burn

Grinding burn is a common phenomenon of thermal damage that has been one of the main constraints in grinding in respect of high efficiency and quality. An acoustic emission (AE) technique was tried in an attempt to identify grinding burn on-line. However, the AE features of grinding burn are relatively weak and are easily obscured by other AE sources. This paper presents an investigation of the AE features of the thermal expansion induced by laser irradiation, which was designed to simulate grinding thermal behaviour. By using wavelet packet transforms, AE features at the grinding burn temperature can successfully be extracted without other mechanical interferential factors. Such thermal AE features provide a firm foundation for analysing and monitoring the AE features of grinding burn.     

Monitoring of acoustic emission during the welding process

Abstract

Acoustic emission (AE) measurements were carried out during submerged-arc welding, in order to verify the capability of AE to detect crack growth during welding.

Using copper powder and different welding parameters, a number of ‘hot’ and ‘cold’ cracks were obtained in welds on A516 CrMo steel plates.

The results showed that some of the AE signals recorded during and after welding were due to the flaws. The possibility of locating events due to the flaws, along the weld seam, was also demonstrated.     

声发射技术在砂轮回转实验破碎识别中的应用

采用一种新型砂轮破碎状态信号检测方法——声发射（AE）技术,通过采集环境背景、空转、空载和负载AE信号,并对信号进行时频域研究。对比砂轮破碎后和破碎前AE信号特征,时域信号特征电压值增大了2.5倍、能量谱峰值增大了4倍、均方根值（RMS）增加了1.17倍,频域信号经快速傅里叶转换（FFT）后得到了一个高频电压信号。研究结果表明:声发射技术优于目前常用的振动法、音量法和光电法,更适合用于砂轮回转实验破碎识别。