Analysis of acoustic emission in precision and high-efficiency grinding technology

Current demands of machining hard and brittle materials at very small tolerances have predicated the need for precision and high-efficiency grinding. In situ monitoring systems based on acoustic emission (AE) provide a new way to control the surface damage and integrality of the components. However, a high degree of confidence and reliability in characterizing the manufacturing process is required for AE to be utilized as a monitoring tool. The authors established AE based online monitoring system and studied technique parameters versus the waveforms of AE under different working conditions. The results show that there are obvious mapping relations between the technique parameters of grinding and the effective values of the AE signals. Grinding along different directions would result in different strength of AE signal. Comparing with grinding along first longitude, fewer AE signal is released when grinding along latitude and better surface quality is generated. Similar variation tendency is observed no matter between AE root mean square (RMS) and linear speed or between surface roughness and linear speed which justify some kind of correlation may exist between AE RMS and surface roughness. The distance between the AE transducer and the AE source should be less than 80 mm while monitoring the process of grinding composite ceramics.     

Online monitoring of precision optics grinding using acoustic emission based on support vector machine

This paper aims to accomplish online monitoring of precision optics grinding with processing condition factors based on theoretical analysis and through grinding experiments. The model for monitoring surface quality of optical elements online (OSQMM) which contains identification model (IM) and interpolation·factor-support vector regression (i?f-SVR) is proposed. IM is applied to analyze and determine which kind of processing condition factors and which kind of its feature parameters are the best one to be used for online monitoring. i?f-SVR which contains the effect factor (fe) and interpolation function (I) to overcome the drawbacks of existing SVR models is applied to predict the monitoring thresholds. The grinding experiments were designed and performed. The influences of technological parameters (e.g., grain size of the grinding wheel, grinding depth, speed of the grinding wheel, speed of the worktable, and materials of workpiece) and processing condition factors (e.g., acoustic emission, grinding force, and vibration) on the surface quality were investigated and analyzed by IM. i?f-SVR was trained and established by the data which were gained through the experiments. After that, the other grinding experiments were performed to apply and verify OSQMM. The results were that the accuracy of alarm for roughness was 85.19 % and the accuracy of alarm for surface shape peak–valley value was 75.93 %. The results show that this method can be effectively applied to monitor the precision optics grinding process online.     

细长杆车削浅析

细长杆车削加工中刀具与工件间的相对振动是影响加工精度的主要原因，而减小振动是主要难题。系统地分析研究了实际生产中采取的各种用于抑制振动的措施，并对可用于细长杆车削振动分析的理论进行了探讨。     

Monitoring force in precision cylindrical grinding

Aerostatic spindles are used in precision grinding applications requiring high stiffness and very low error motions (5–25 nm). Forces generated during precision grinding are small and present challenges for accurate and reliable process monitoring. These challenges are met by incorporating non-contact displacement sensors into an aerostatic spindle that are calibrated to measure grinding forces from changes in the gap between the rotor and stator. Four experiments demonstrate the results of the force-sensing approach in detecting workpiece contact, process monitoring with small depths of cut, detecting workpiece defects, and evaluating abrasive wheel wear/loading. Results indicate that force measurements are capable of providing useful feedback in precision grinding with excellent contact sensitivity, resolution, and detection of events occurring within a single revolution of the grinding wheel.     

Chatter classification by entropy functions and morphological processing in cylindrical traverse grinding

During cylindrical traverse grinding processes, two types of regenerative chatter—workpiece and grinding wheel—may degrade the accuracy of the surface finish. To maintain productivity and quality, a closed-loop vibration control system should be provided for the grinding system. An algorithm for automated classification by type is essential in developing such a system. In cylindrical traverse grinding, the chatter vibration signals display unstable dynamic characteristics, which makes the task of chatter classification especially difficult. This paper introduces an approach that combines entropy techniques with morphological preprocessing to classify traverse grinding regenerative chatter by type based on the vibration spectrum. Experimental data analysis is used to demonstrate that the proposed method can effectively distinguish workpiece regenerative chatter from wheel regenerative chatter. Because both the entropy function and morphological processing are computationally easy, this method is not only readily understood, but also conveniently adaptable to system expansion and real-time applications.     

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.     

Evaluation of grinding wheel loading phenomena by using acoustic emission signals

In the industrial manufacturing field, machining is a major process. Machining operations involve grinding, drilling, milling, turning, pressing, molding, and so on. Among these operations, grinding is the most precise and complicated process. The surface condition of the grinding wheel plays an important role in grinding performance, and the identification of grinding wheel loading phenomena during the grinding process is critical. Accordingly, this present study describes a measurement method based on the acoustic emission (AE) technique to characterize the loading phenomena of a Si₂O₃ grinding wheel for the grinding mass production process. The proposed measurement method combines the process-integrated measurement of AE signals, offline digital image processing, and surface roughness measurement of the ground workpieces for the evaluation of grinding wheel loading phenomena. The experimental results show that the proposed measurement method provides a quantitative index from the AE signals to evaluate the grinding wheel loading phenomena online for the grinding mass production process, and this quantitative index is determined via some experiments in advance in the same grinding environment to help the monitoring and controlling of the grinding process.     

In-process force measurement for diameter control in precision cylindrical grinding

This article shows the theory and implementation of a force measurement-based approach to controlling workpiece diameter in cylindrical grinding. A simple model proposed is used to relate infeed velocity to grinding force. The model is extended to accurately control the amount of material removed in outer diameter plunge grinding given the normal force, which may be monitored in real-time. The model incorporates the key parameters, including the structural loop stiffness, the plunge infeed velocity, and the wheel and workpiece properties. However, only the infeed velocity must be explicitly known. The contribution of this work is experimental validation that the lag between infeed and stock removal can be predicted using force feedback without a priori knowledge of the grinding system. This allows very accurate diameter control (0.25 μm of nominal), even in the presence of thermal drift, wheel wear, and machine error.     

精密球研磨加工中振动信号的检测与分析

由于轴承精密球的精度直接影响轴承的运动精度及寿命,利用加速度计传感器、数据采集卡和Lab VIEW,设计了一套测试振动信号的检测设备,对精密球在研磨加工过程中产生的振动信号进行了测试,从时域、频域等多个角度进行了分析论证.分析结果表明,利用精密球体研磨加工中产生的振动信号能有效地监控整个研磨加工过程,并能实时地反映球体研磨的状态和精度.     

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.     

Study on precision grinding of screw rotors using CBN wheel

With increasing demands for high-speed and high-precision machining technology, CBN shape grinding is an effective means in the field of precision machining for screw rotors. Aiming at the high precision machining of screw rotors, a mathematical model for the axial profiles of the CBN wheel for machining screw rotors is developed based on theory of gear engagement. Small electroplated CBN wheel is firstly used to grinding screw rotors. Taking the backlash of screw rotors and the coating thickness of CBN layer into consideration, the modification of the base body of the wheel shape is introduced into the design of CBN wheel. For reducing the tooth profile errors of screw rotors induced by mounting errors and wears of CBN wheel, a mathematical model of the error analyses is established and the influence curves of the profile errors affected by mounting errors and radius error of grinding wheel are proposed. The electroplated CBN wheels for the screw rotors are made to verify the validity and effectiveness of the presented method and the machining experiments were performed. Results of this study reveals that the method proposed in this paper can be used as the precision grinding of screw rotors.     

In-process prediction of the hardened layer in cylindrical traverse grind-hardening

Grind-hardening is an innovative manufacturing process that takes advantage of the high amount of heat generated in the contact zone to produce a martensitic phase transformation in the subsurface layer of the workpiece. However, for a successful industrial implementation of the process, the closed loop control of the hardening depth is essential. Firstly, in this paper, cylindrical traverse grinding tests and metallographic analysis are conducted, and a grinding parameter that enables the in-process control of the hardness penetration depth (HPD) is proposed. Secondly, a nondestructive method based on the Barkhausen noise technique is presented as a quality control procedure for the HPD estimation.     

Online tool condition monitoring in turning titanium (grade 5) using acoustic emission: modeling

This paper presents an online prediction of tool wear using acoustic emission (AE) in turning titanium (grade 5) with PVD-coated carbide tools. In the present work, the root mean square value of AE at the chip–tool contact was used to detect the progression of flank wear in carbide tools. In particular, the effect of cutting speed, feed, and depth of cut on tool wear has been investigated. The flank surface of the cutting tools used for machining tests was analyzed using energy-dispersive X-ray spectroscopy technique to determine the nature of wear. A mathematical model for the prediction of AE signal was developed using process parameters such as speed, feed, and depth of cut along with the progressive flank wear. A confirmation test was also conducted in order to verify the correctness of the model. Experimental results have shown that the AE signal in turning titanium alloy can be predicted with a reasonable accuracy within the range of process parameters considered in this study.     

Statistical process control of acoustic emission for cutting tool monitoring

The problem of cutting process monitoring has been investigated in recent years, with encouraging results, using pattern recognition analysis of acoustic emission (AE) signals. The analyses are based on linear discriminant functions, which assume that the observed data (from each class) are independent random samples from multivariate normal distributions with equal covariance matrices. However, in a number of practical situations some (or all) of these assumptions may not necessarily hold, resulting in errors in the analysis.In this paper, the distributions of AE spectra generated in earlier work are first analysed, and the results indicate departure from the assumptions, although the lack of normality was not too severe. Relaxing the assumption of equality of the covariance matrices, quadratic discriminant function analysis produced improved results for tool wear and chip noise monitoring while degrading tool fracture detection. The latter is due to inadequacy of the amount of data used in training the system. It is expected that increasing the data base would improve the results for all classes.The analysis until now has focused on reducing the dimensionality of the feature space by eliminating the features with the least discriminatory power. Even though this inevitably reduces the performance of the system, it is a necessary compromise for increased computational speed. To make use of the entire feature set with a reduced matrix rank, a principal component analysis is investigated. The result is a substantial improvement in correct classification of AE signals, even under different cuting conditions.     

基于声发射技术飞机关键部件健康监测方法

为解决飞机关键结构部件疲劳损伤的有效监测,及时发现潜在的安全隐患,避免灾难性事故的发生.对于采用先进声发射技术所监测到的某飞机水平尾翼的原始声发射信息,提出采用小波包分析与支持向量机相结合的方法对匕机水平尾翼的健康状况进行识别与诊断.该方法将飞机水平尾翼产乍的原始声发射信号进行多级小波包分解,提取其频带能量作为特征向量,输入到由支持向量机构建的健康监测器对其进行健康识别与诊断.实验结果表明,该方法可以有效、准确地识别并诊断出飞机水平尾翼的疲劳裂纹,为飞机结构部件健康状态的有效监测提供了新途径.     

Detection of faults in gearboxes using acoustic emission signal

Vibration analysis is widely used in machinery diagnosis, and wavelet transform and envelope analysis have also been implemented in many applications to monitor machinery condition. Envelope analysis is well known as a useful tool for the detection of rolling element bearing faults, and wavelet transform is used in research to detect faults in gearboxes. These are applied for the development of the condition monitoring system for early detection of the faults generated in several key components of machinery. Early detection of the faults is a very important factor for condition monitoring and a basic component to extend CBM (Condition-Based Maintenance) to PM (Prediction Maintenance). The AE (acoustic emission) sensor has a specific characteristic on the high sensitivity of the signal, high frequency and low energy. Recently, AE technique has been applied in some studies for the early detection of machine fault. In this paper, a signal processing method for AE signal by envelope analysis with discrete wavelet transforms is proposed. Through the 15 days test using AE sensor, misalignment and bearing faults were observed and early fault stage was detected. Also, in order to find the advantage of the proposed signal processing method, the result was compared to that of the traditional envelope analysis and the accelerometer signal.     

Particle monitoring method using acoustic emission signal for analysis of slider/disk/particle interaction

One of the most important reliability issues in an information storage device is the contamination problem. The slider and disk can be damaged by the particles intruded into the slider/disk interface (SDI). In this work, in order to monitor the slider/disk interaction due to particle injection the acoustic emission (AE) method, which is typically utilized for the detection of slider contact, was used. The raw as well as frequency spectrum of the AE signal were obtained during the particle injection test. The particles were artificially injected inside the test apparatus to simulate the effect of contamination on the slider/disk interaction. SiC and polystyrene particles were used for the tests. As a result, the 1st torsional and bending mode frequencies of the nano-slider were observed when 1 μm SiC particles and 60 nm polystyrene particles were injected into the SDI. Also, it was shown that the particle behavior at the SDI can be predicted from the characteristics of the AE raw signal.     

Ring wear monitoring in IC engines: an acoustic emission approach

Monitoring the condition of the engine compression ring in an engine operation is very important since it affects the engine performance. One of the most promising ring wear monitoring methods is based on the analysis of acoustic emission (AE) signals, which is an extremely powerful technique that can be deployed in a wide range of applications of non-destructive testing [Vallen Systeme (2000)]. This technique is already used for monitoring tool wear almost in all machining operations, but in this study, the AE signals were applied for monitoring ring wear in internal combustion (IC) engines. The AE signals generated in the ring sliding zone are very sensitive for correlation with ring wear, which in turn affects ring performance. This study was carried out with a single compression ring mounted on the piston. The AE signals were analyzed by considering signal parameters such as ring down count and RMS voltage [Krzysztof Jemielniak (2000) J Mater Process Technol 4752:1–6]. Analysis showed that the AE signal technique is applicable for ring wear monitoring in IC engines.