Smart portable noninvasive instrument for detection of internal air leakage of a valve using acoustic emission signals

Acoustic emission (AE) can be used to detect and determine the internal leakage rate through a valve in many applications. However, a general AE data acquisition system is expensive and bulky. This paper presents a novel low-cost instrument based on microcontroller and a novel theoretical model based on AE technique to predict the leakage rate. The system is an embedded system instead of a general PC-based data acquisition. AE_RMS parameter is used to infer the leakage rate, and the effects of various process variables on the model are also studied. The experimental results have shown that the instrument is capable of detecting possible valve leakage encountered in online operation. With its portability, ease of use and compactness, the proposed system provides faster and low cost valve leakage detection.     

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.     

切入磨削与纵向磨削的磨削力分析与比较

研究了同时包含切入磨削和纵向磨削的复杂外圆磨削过程。根据纵向磨削过程的特点,将砂轮等效成若干个小砂轮,在传统阶梯模型的基础上构建了砂轮磨损的抛物线模型。推导了基于两种模型的纵向磨削切向分力和切入磨削切向分力的比较公式,两切向分力的比值反映了切入磨削和纵向磨削转换时切向分力的变化情况,它主要与磨削系数、砂轮宽度和纵向进给速度有关。采用砂轮主轴功率信号分析磨削切向分力,通过实验验证了抛物线模型更符合实际情况的结论。研究结果为采用磨削力信号和功率信号研究复杂磨削过程的监控提供了参考依据。     

砂轮精确修整时的声发射检测方法

砂轮修整状态严重地影响砂轮的磨削性能,这在精磨及成形磨削过程中显得尤为突出。本文提出了一种砂轮精确修整的新方法,利用声发射试验检测系统实时提取砂轮修整过程中声发射信号的特征参量,有效地实现了砂轮型面的精确修整。     

Investigation of the relationship between internal fluid leakage through a valve and the acoustic emission generated from the leakage

The detection of acoustic emission (AE) signals produced by liquid and gas leakage through valves can be related directly to the qualitative leakage rate. This allows for cost estimation of losses in processes for several industries. However, to find out the relationship between qualitative leakage rate and AE signal large amounts of experimental data is needed. This paper presents a theoretical investigation of the acoustic emission to detect the internal leakage rate through a valve and experimental validation. The AE signals generated by internal liquid and gas leakage through valves were characterised. The effect of the influenced factors of leakage rates, inlet pressure levels, valve sizes and valve types, on AE parameter, AE_RMS, were studied and explained. The results of theoretical and experimental showed that AE signal power computed from the power spectral density (PSD) correlated well with influenced factors of leakage rates. Finally, a novel and inexpensive AE instrument has been invented for predicting qualitative leakage rate using a micro processor and derived relationship.     

砂轮磨钝监测及修整

利用声发射(AE）信号归原处理法对砂轮磨钝程度进行监测，在此基础上开发了砂轮磨钝监测及自动修整系统。该系统可以对小批量、多品种工件磨削过程中砂轮钝化进行有效的监测，采用的CNC砂轮自动修整器能够解决多种类型成型砂轮的修整，具有一定的通用性和实用性，能实现磨削砂轮监测、修整过程的自动化和智能化。     

IN-PROCESS TOOL CONDITION MONITORING USING ACOUSTIC EMISSION SENSOR IN MICROENDMILLING

Recently researchers and manufacturers have shown keen interest in fabricating micro-components through tool based mechanical micromachining processes namely micromilling, microdrilling, microturning, etc. In this scenario, microendmilling is used in the manufacture of micro-molds, micro-dies, micro-channel, micro-gear, etc. The major issue in microendmilling process is the unpredictable life of the micro-tool and its premature failure during operations. Therefore in this work, an attempt has been made to monitor the tool condition (in-process) using acoustic emission (AE) sensor in microendmilling of different materials such as aluminum, copper and steel alloys. From this study, it is observed that there is a strong relationship between the tool wear (flank wear) and acoustic emission (AE_RMS) signals, surface roughness (R_a) as well as chip morphology. In order to understand the mechanism of tool wear, SEM and EDAX analyses were carried out on the microendmill after machining. Scanning Electron Microscope (SEM) and energy dispersive X-ray spectroscopy (EDAX) analyses indicated occurrence of the tool wear mechanism such as adhesion and plastic deformation in all three materials. Coating delamination is also observed while machining steel alloy. This work provides significant and new knowledge on the usage of AE sensor in monitoring the tool condition and understanding the tool wear mechanism in microendmilling of different materials.     

Experimental study of burn classification and prediction using indirect method in surface grinding of AISI 1045 steel

Grinding burn is a discoloration phenomenon according to the thickness of oxide layer on the ground surface. This study tries to establish an automatic grinding burn detection system with robust burn features that are caused by burn and not by the design parameters. To address this issue, a method based on acoustic emission sensor, accelerator, electric current transducers, and voltage transducers was proposed in an attempt to extract burn signatures. A trial-and-error experimental procedure was presented to find out burn threshold. Vitrified aluminum oxide grinding wheel and AISI 1045 steel workpiece were used in the grinding test, as they were the most commonly used wheel–workpiece combinations in conventional grinding process. With the help of fast Fourier transform and discrete wavelet transform, the spectral centroid of AE signal, the maximum value of power signal, and the RMS of the AE wavelet decomposition transform from wavelet decomposition levels d1 to d5 were extracted as burn features. The spectral centroid of AE signal was believed not to be affected by grinding parameters. A classification and prediction system based on support vector machine was established in order to identify grinding burn automatically. Results indicate that the classification system performs quite well on grinding burn classification and prediction.     

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。     

无心外圆磨削能耗建模及优化研究

无心外圆磨削广泛应用各类棒料工件的精加工,其在加工过程中会产生巨大的能量消耗,为提高无心外圆磨削过程的能量效率,主要对磨削过程的工艺参数进行了优化。在考虑磨削功率、表面粗糙度及磨削用量约束的基础上,将砂轮线速度、导轮线速度、导轮架进给速度选为优化变量,以最小能量消耗为优化目标来建立数学模型;提出了蜜蜂进化型遗传算法并结合MATLAB软件对模型进行优化求解,将结果与传统遗传算法进行对比实验,验证了蜜蜂进化型遗传算法的有效性。     

无心磨削过程碳排放量及其关键影响因素解析

准确量化制造过程中的碳排放量是实现低碳制造的前提条件。磨削是一种广为应用的精密加工方法,同时也是切削热量高、使用切削液量大、加工环境差、能量利用效率低的加工工艺。利用公理化设计找出影响低碳磨削的各层关键影响因素,综合考虑了能源、资源和废弃物对碳排放量的影响,建立了数控粗磨、精磨和光磨整个磨削过程的ERWC碳排放量定量分析模型。最后进行了实际磨削加工测量实验,并用MATLAB给出了模型计算比较结果,解析了关键影响因素。     

An investigation of acoustic emission to monitoring flat lapping with non-replenished slurry

Lapping is a precision manufacturing process. However, the material removal rate and surface roughness show significant variation between trials for repeated experiments and, thus, the repeatability of the results depends on the machine operator’s skill. Acoustic emission (AE) seems to be capable of monitoring the process. Therefore, an understanding of AE generation during lapping is important to predict the performance of the grains and hence the lapping results. Based on a theoretical analysis and experimental results collected during flat lapping, the AE signal was investigated for the situation when slurry is supplied without replenishment. The experiments were carried out with a wireless rotating AE sensor mounted in the middle of the lapping plate. Three parameters related to the AE curve are proposed to monitor the process. The influence of process parameters (lapping pressure, velocity, average grain size, concentration of grains in lapping compound and the number of conditioning rings) on the characteristics of the AE curve was investigated.     

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.     

在MQ1350A磨床上磨削高精度抛光辊

通过选用合理的砂轮、磨削工艺参数、磨削液等并进行适当的修整砂轮、调整与平衡磨床,即可在MQ1350A普通外圆磨床上磨削修复进口1450mm冷轧机抛光辊。经分析磨削结果可知,在MQ1350A普通外圆磨床上磨削高精度要求的进口1450mm冷轧机抛光辊是完全可行的。     

圆弧形砂轮精密修整及其声发射在线监测技术

针对球面、非球面及自由曲面超精密磨削加工用圆弧形金刚石砂轮难以精密修整的问题,提出基于旋转绿碳化硅（GC）磨棒的端部在位精密修整方法及修整过程的声发射在线监测技术。基于圆弧形金刚石砂轮的结构特性,制订圆弧形金刚石砂轮的在位精密修整与修整过程的声发射在线监测技术方案。依据修整与在线监测方案,对D64圆弧形金刚石砂轮进行修整实验及其声发射信号采集,修整后跳动误差小于10μm,比修整前减小30μm左右,砂轮精度显著提高。利用声发射信号均方根值获取砂轮修整结束的特征预警阈值,实现了旋转GC磨棒端部在位精密修整过程的在线监测以及修整结束时间的准确判断,可以有效提高球面非球面磨削加工过程的效率。     

ELID磨削氧化膜控制方案的设计与研究

微处理器为处理控制单元,通过ELID精密磨削技术对金属基砂轮进行在线修整,改变砂轮氧化膜厚度,从而调整ELID磨削状态.该方案自动处理数据,判断磨削膜厚状态,改变电解电源参数,使砂轮始终保持锋利状态.此方法实现磨削过程无人控制,砂轮连续自动修整,消除传统磨削加工停机修整砂轮的弊端,提高磨削效率,减少砂轮过快损耗.     

Acoustic emission as an indicator of material-removal regime in glass micro-machining

Acoustic emission (AE) spectra were recorded during microgrinding of brittle materials. It was found that the specific AE energy (i.e., the measured AE energy divided by the material removal rate) was lower for fracture-dominated grinding than for plastic flow-dominated grinding. Two subsequent experiments were performed to measure AE energy while holding the material-removal rate constant. By controlling either the critical depth of cut (for ductile-brittle transition) of the workpiece material, or the actual depth of cut of the grinding machine, the sensitivity of AE energy to grinding regime was investigated for grinding with a constant material-removal rate. Contrary to conventional thinking about the relative contributions of plastic flow and fracture in generation of AE activity, it was found that the AE energy was larger in ductile-regime grinding than in brittle-regime grinding, for identical material removal rates. As a result of the experiments described in this paper, it can be concluded that AE energy measured during microgrinding is sensitive to changes in the mechanism of material removal. For a given volume of material removed, there is more AE energy in a plastic flow-dominated process than in a fracture-dominated process. The relationship found between AE energy and material removal regime could lead to an in-process sensing strategy for controlling grinding ductility.     

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.     

加工齿轮滚刀齿形过程中如何提高齿形精度

针对阿基米德滚刀加工渐开线圆柱齿轮,用阿基米德造型的滚刀在刃磨滚刀齿形时,为了提高齿形精度,改变加工工艺和检测工艺,使用先进的"砂轮修正器"和"齿轮测量中心",缩短了设计和加工时间,提高了齿轮滚刀齿形磨制的效率,保证了滚刀加工渐开线圆柱齿轮齿形精度,使大于4模数的阿基米德滚刀齿形(A级﹑AA级)易于保证被加工渐开线圆柱齿轮的齿形精度。