Grinding Burn Detection Based on Cross Wavelet and Wavelet Coherence Analysis by Acoustic Emission Signal

Grinding burn monitoring is of great importance to guarantee the surface integrity of the workpiece. Existing methods monitor overall signal variation. However, the signals produced by metal burn are always weak. Therefore, the detection rate of grinding burn still needs to be improved. The paper presents a novel grinding burn detection method basing on acoustic emission(AE) signals. It is achieved by establishing the coherence relationship of pure metal burn and grinding burn signals. Firstly, laser and grinding experiments were carried out to produce pure metal burn signals and grinding burn signals. No-burn and burn surfaces were generated and AE signals were captured separately. Then, the cross wavelet transform(XWT) and wavelet coherence(WTC) were applied to reveal the coherence relationship of the pure metal burn signal and grinding burn signal. The methods can reduce unwanted AE sources and background noise. Novel parameters based on XWT and WTC are proposed to quantify the degree of coherence and monitor the grinding burn. The grinding burn signals were recognized successfully by the proposed indexes under same grinding condition.     

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.     

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.     

Workpiece roundness profile in the frequency domain: an application in cylindrical plunge grinding

In grinding, most control strategies are based on the spindle power measurement, but recently, acoustic emission has been widely used for wheel wear and gap elimination. This paper explores a potential use of acoustic emission (AE) to detect workpiece lobes. This was achieved by sectioning and analysing the AE signal in the frequency domain. For the first time, the profile of the ground workpiece was predicted mathematically using key frequencies extracted from the AE signals. The results were validated against actual workpiece profile measurements. The relative shift of the wave formed on the surface of the part was expressed using the wheel-workpiece frequency ratio. A comparative study showed that the workpiece roundness profile could be monitored in the frequency domain using the AE signal during grinding.     

模拟磨削烧伤声发射信号的实验研究

人们一直试图运用声发射技术来在线识别磨削烧伤,但磨削烧伤的声发射信号相对较弱,容易被磨削中其它声发射信号所掩盖。为了在发现磨削烧伤声发射信号,对镍基合金和陶瓷材料热扩散的声发射信号特征进行了研究,该热扩散由激光照射引起,来模拟磨削热效应。运用短时傅里叶变换技术,在没有其它干扰因素的条件下,模拟磨削烧伤温度的声发射信号的频率特征被成功的提取出来。发现材料温度改变越大则声发射信号越强!     

外圆磨削砂轮形貌仿真与工件表面粗糙度预测

对磨削砂轮形貌、外圆磨削过程及工件表面形貌进行了仿真,实现了对工件表面粗糙度的预测,并对仿真模型进行了验证。采用Johnson变换和Gabor小波变换,实现了高斯域和非高斯域的转化,在随机域内对磨削砂轮形貌进行了仿真。根据外圆磨削运动过程,通过对砂轮和工件相互作用过程的分析,建立了磨粒运动轨迹方程和工件形貌方程,在考虑磨粒切削、耕犁与摩擦作用的条件下,对外圆磨削过程进行了仿真。建立了外圆磨削模型,实现了对加工工件形貌的仿真和粗糙度预测。     

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。     

Reduction of Air-Grinding Time Using Collaboration of Dual Sensors

Air-grinding time is inevitable in the grinding process. Since the low feedrate during air grinding makes the process inefficient, air-grinding time should be reduced. This paper presents a technology for reducing air-grinding time in cylindrical plunge grinding operations. The main idea is to estimate a distance, NAP (nearest approaching point), between the workpiece surface and the position of the grinding wheel. After detecting the NAP using an ultrasonic sensor, a CNC controller adjusts the feedrate using the feedrate override function with the help of the AE signal that gives the CP (contact point) information. The experimental results show that an ultrasonic and an AE sensor are good enough for detecting the NAP and the CP, respectively, and the system reduces the conventional air-grinding time by two-thirds.     

Stability analysis for SiC grinding based upon harmonic wavelet and Lipschitz exponent

Abstract

In order to effectively identify the stability degree of grinding and obtain better surface integrity of machined workpiece in grinding application, in this paper, a quantitative assessment approach based on harmonic wavelet and Lipschitz exponent is proposed, using silicon carbide as an example. To begin with, the Lipschitz exponent is defined and calculated by harmonic wavelet modulus maxima (HWMM) and linear regression algorithm, respectively. Then the Kurtosis based on Lipschitz exponent (KLE) is defined as a quantitative indicator. In grinding experiment, the acceleration signals and grinding force signals of the X/Y directions are selected, and experimental parameters including wheel speed, workpiece speed and grinding depth are employed to investigate the grinding stability in grinding stage. Experimental results show that the grinding stability is relatively worse at low wheel speed, low-high workpiece speed and large grinding depth in terms of KLE index, which may provide a new guidance to explore optimized grinding parameters and thus obtain good surface integrity in engineering applications.     

锥面砂轮磨齿机磨齿金属磨除率分析

通过对 Y715 0 D锥面砂轮磨齿机磨削过程的分析 ,分析了砂轮与工件的运动 ,进而讨论砂轮在磨削过程中金属磨除率及其变化情况 ,导出瞬时金属磨除率的计算公式。这对于研究磨削力、避免磨削裂纹、磨削烧伤等磨削缺陷是非常有意义的     

Real-time detection of the breakage of small diameter drills with wavelet transform

This paper presents a real-time tool breakage detection method for small diameter drills using acoustic emission (AE) and current signals. Using the transmitted properties of the AE signal, apparatus for detecting the AE signal for tool breakage monitoring was developed for a machine centre. The features of tool breakage were obtained from the AE signal using typical signal processing methods. The continuous wavelet transform (CWT) and the discrete wavelet transform (DWT) were used to decompose the spindle current signal and the feed current signal, respectively. The tool breakage features were extracted from the decomposed signals. Experimental results show that the proposed monitoring system possessed an excellent real-time capability and a high success rate for the detection of the breakage of small diameter drills using combined AE and current signals.     

运用小波变换检测汽车后桥总成故障

用傅里叶变换和小波变换寻找并分解出最能反映汽车后桥故障的频率段，再从该频率段的重建信号中提取最能反映故障的特征（包括信号的方差和峰态）。为了寻找最优判断故障的指标，从现场采集的后桥总成振动信号数据库中抽取一定样本的正常件和故障件，并提取每个工件信号的特征数据，用支持向量机神经网络可得到最优的分类指标。     

氮化硅陶瓷镶块低粗糙度磨削的研究

提出氧化铝砂轮磨削陶瓷表面的加工过程是砂轮磨粒与工件表面凸峰的碰撞－碰撞与摩擦共同作用－摩擦抛光。对砂轮速度，工件转速、砂轮横向进给量、光磨次数，陶瓷材料硬度以及切削液等因素对表明粗糙度的影响进行了分析，表明氧化铝砂轮通过挤压和磨削抛光作用使陶瓷工件表面的粗糙度得到显著改善，实现了在普通磨床上对陶瓷材料的高质量加工。     

Influence of wheel speed on surface finish and chip geometry in precision contour grinding

A geometric computer model of a precision grinding operation was developed to calculate the surface features generated during contour grinding with a radiused wheel. This simulation includes the influence of the wheel (rotational speed, diameter, and nose radius), the workpiece (radius at cutting point, rotational speed), and the feedrate of the grinding wheel over the part. The model indicates that small changes in the wheel speed relative to the workpiece can have a dramatic effect on the surface finish over a specific area. Analysis of ground surfaces reveals uniform surface profiles and easily distinguished features that could only be produced by a constant wheel speed. This occurs for an air-bearing, air-turbude grinding spindle that has limited torque and is driven under open-loop control. The effects of the relative speeds are analyzed and an energy-based “phase locking” mechanism is proposed that can provide feedback to the grinding spindle from the material removal operation. By monitoring the spindle speeds during the grinding process and evaluating the resulting surface features, the phase effect has been experimentally verified.     

超声ELID复合内圆磨削时电参数对氧化膜的影响

通过Labview对试验中电压电流的测试,用VHX-2000超景深显微镜对氧化膜进行观测,分析了超声ELID复合磨削电参数与砂轮表面氧化膜之间的关系。研究结果表明,电参数的选择决定了砂轮结合剂发生电解反应溶解去除的速度以及砂轮表面生成氧化膜的厚度,进而影响砂轮的磨削性能和工件的磨削质量。     

细长轴磨削变形的变速优化智能预测控制研究

在建立细长轴磨削过程中工件弹性变形数学模型的基础上，打破了传统的恒速控制方法，提出了一种控制细长轴磨削弹性变形的变速优化适应控制策略：根据磨削系统沿工件轴向各点刚度的不同，通过不断改变工件转速和纵向进给速度，控制法向磨削力的变化，进而控制工件的弹性变形；同时，由一个神经网络预测系统和一个模糊控制系统实时控制加工过程中的磨削深度，进一步控制加工中由于砂轮磨损而引起的细长轴形状误差。仿真和实验结果表明：变速优化适应控制策略和模糊神经网络预测控制方法是可行的，可极大地提高磨削生产率，减小细长轴的形状误差。     

刀具磨损声发射信号处理中小波基选取的研究

通过对小波基性质和刀具磨损声发射（AE）信号特点的研究,从理论上分析了小波变换中刀具磨损AE信号处理中小波基选取的方法。在试验验证过程中,根据小波包信号分解遵循能量守恒原理,用四种小波基对刀具磨损AE信号进行三层小波包分解;以AE信号经小波包分解后各频带上的能量为特征参数,比较四种情况下特征参数的变化,验证了理论分析的正确性。     

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。     

离子液体在微量润滑磨削界面的摩擦学机理研究

采用电镀CBN砂轮,以镍基合金GH4169为工件材料,实验研究了两种离子液体的微量润滑磨削加工性能,分别是1-丁基-3-甲基咪唑四氟硼酸盐(［BMIM］BF4)和1-己基-3-甲基咪唑四氟硼酸盐(［HMIM］BF4),并采用分子动力学模拟,揭示了离子液体在磨粒/工件界面物理吸附膜的形成机制,进一步开展了工件已加工表面的X射线光电子能谱（XPS）分析,揭示了离子液体在磨粒/工件界面化学反应膜的形成机制。研究结果表明：上述两种离子液体适合作为磨削液应用于微量润滑磨削加工中,既能较干磨大幅降低磨削比能和磨削力比,提高工件已加工表面质量,又能较干磨大幅降低磨削温度达100 ℃以上,避免磨削烧伤;磨粒磨钝表面由于微破碎所形成的凹槽状断口是离子液体进入磨粒/工件界面的输运通道,离子液体分子通过吸附在凹槽状断口内形成边界润滑膜,通过减小磨粒工件之间的直接接触面积来减小摩擦力;在微量润滑磨削加工过程中,以上两种离子液体均与工件在磨削界面上发生化学反应,形成了氟化物与氧化物共存的化学反应膜。     

A NEW TECHNOLOGY ON ENHANCING HEAT TRANSFER AT GRINDING ZONE THROUGH JET IMPINGEMENT DURING CREEP FEED GRINDING

This paper presents a jet-impingement technology to enhance heat transfer at the grinding zone. The jet-impingement technology uses a new apparatus developed to spray grinding fluid onto the workpiece surface at the grinding zone from the radial holes of an electroplated CBN wheel. Because the fluid is sprayed normally on the workpiece surface at the grinding zone, the heat transfer to the fluid is significantly improved as compared to the conventional fluid delivery methods. Experimental results, obtained under both imitated grinding tests and actual creep-feed grinding of titanium alloys, show that the jet-impingement technology lowers grinding temperature significantly. Much higher material removal rates are possible with jet-impingement without workpiece burn.