Tool condition monitoring by acoustic analysis through a solid path

A sensing method using an acoustic signal obtained in a relative low frequency range through a solid path for the monitoring of tool wear has been investigated. Such acoustic signals could be in the form of stress waves that are released during a machining process, which can be picked up by a regular ferroelectric microphone. Data analysis was conducted in both time and frequency domains. A clear pattern in such signals corresponding to the tool wear conditions has been identified. Several components in spectra were found in the pattern for indicating sudden changes of tool wear or breakage occurring at major cutting edges. It was also observed that the RMS and variance values of the signals could indicate the specific wear condition of the tool. Therefore, this kind of acoustic signal carries sensitive information about the progress of tool wear and can be implemented on line for monitoring tool wear.     

基于广义分形维数的刀具磨损状态监测

根据多重分形理论,提出一种刀具磨损在线监测方法。采用覆盖法计算了切削加工过程中声发射（AE）信号的广义维数,得到了不同刀具磨损状态下AE信号的广义维数谱,分析了广义维数与刀具磨损状态间的关系。计算了AE信号广义维数特征距离及广义维数相关系数,通过比较各广义维数相关系数的大小,对刀具磨损状态进行了决策分类。实测信号验证结果表明,运用该方法可以对刀具磨损状态进行有效识别。     

Tool wear monitoring using acoustic emission in the existence of chatter

A method using an acoustic emission (AE) technique to monitor automatically a tool wear was examined. A flank wear is calculated using a statistical model which uses RMS value of AE signal and cutting speed as variables. The investigation reveals that the AE signal is influenced by tool vibration, specially during chatter. This contradicts the theory that the AE signal is not susceptible to mechanical vibration. The paper discusses the mechanism that explains the effect of the tool vibration on the AE signals.     

On-line tool condition monitoring in face milling using current and power signals

The vast majority of tool condition monitoring systems use the cutting force as the predictor signal. However, due to prohibitive cost to performance ratios and maintenance and operational problems, such methods are not favoured by industries. In this paper, a method for continuous on-line estimation of tool wear, based on the inexpensive spindle motor current and voltage measurements, is proposed for the complex and intermittent cutting face milling operation. Sensors for these signals are free from problems associated with the cutting forces and the vibration signals. Novel signal processing strategies have been proposed for on-line computation of useful features from the measured signals. Feature space filtering is introduced to obtain robust and improved predictors from the extracted features. A multiple linear regression model, built on the filtered features, is then used to estimate tool wear in real-time. Very accurate predictions are achieved for both laboratory and industrial experiments, surpassing earlier results using cutting forces and estimation methods based on complex methodologies such as artificial neural networks.     

在微细电火花铣削中介质对电极损耗的影响

在微细电火花加工（EDM）中电极损耗是不可避免的,而针对电极损耗的研究大都是在油工作液中,很少针对气中放电时的电极损耗进行研究．气中电火花加工普遍采用管状电极,所以为了获得尺寸更小的工件,通过反拷块可磨削出微米级的实心电极,并采用外部充气的方式,可实现微米级三维结构的气中电火花加工．实验考虑了影响气中放电电极损耗的各种因素．通过观察微细电火花三维铣削放电现象与结果,可得到气中放电的规律．由于电火花加工中电极损耗是不可避免的,所以在三维铣削加工中对电极进行在线检测并补偿,工件成形精度大大提高．对刀具路径进行合理规划,可以缩短加工时间．与油中电火花铣削相比,气中电火花加工时电极损耗更低,加工表面质量更好．     

Acoustic emission analysis for tool wear monitoring in face milling

Monitoring the condition of the cutting tool in any machining operation is very important since it will affect the workpiece quality and an unexpected tool failure may damage the tool, workpiece and sometimes the machine tool itself. Advanced manufacturing demands an optimal machining process. Many problems that affect optimization are related to the diminished machine performance caused by worn out tools. One of the most promising tool monitoring techniques is based on the analysis of Acoustic Emission (AE) signals. The generation of the AE signals directly in the cutting zone makes them very sensitive to changes in the cutting process. Various approaches have been taken to monitor progressive tool wear, tool breakage, failure and chip segmentation while supervising these AE signals. In this paper, AE analysis is applied for tool wear monitoring in face milling operations. Experiments have been conducted on En-8 steel using uncoated carbide inserts in the cutter. The studies have been carried out with one, two and three inserts in the cutter under given cutting conditions. The AE signal analysis was carried out by considering signal parameters such as ring down count and RMS voltage. The results show that AE can be effectively used to monitor tool wear in face milling operation.     

Tool health monitoring using acoustic emission

The concept of CIM and FMS has created a whole new area of what is called ‘tool management system’ (TMS). For efficient operation of TMS, a real-time monitoring of tool condition is desirable. Tool health monitoring is critical in today's automated production and is likely to become crucial as we progress towards the unmanned factory of the future. The paper describes the development of a microcomputer-based acoustic emission (AE) system for monitoring progressive wear of a cutting too!. It also includes results of experiments conducted with the system. The state-of-the-art in AE technique and its comparison with other techniques for tool condition monitoring are also presented.     

大齿轮齿形误差在位检测的一种新方法

本文提出了用测量头的直线运动轨迹做基准来在位检测大齿轮渐开线齿形误差的新方法，建立了在统一坐标系中的齿部理论渐开线数学模型和测量头轨迹方程以及经原理误差补偿后的齿形误差计算模型，解决了定位精度问题。这种新方法特别适用于大齿轮齿形误差在位检测仪器的研制和开发。     

截齿磨损程度的多特征信号融合识别研究

为了解决采煤机开采过程中截齿磨损程度在线监测和状态识别的工程难题,提出一种基于多特征信号融合的截齿磨损程度识别方法。搭建截齿磨损程度监测实验台,分别测试提取不同磨损程度截齿截割过程中的振动加速度信号、声发射信号、红外热像信号和电机电流信号,建立了截齿截割的多传感信号数据样本库;针对数据样本库中两相邻磨损状态截齿特征样本存在数据交集、系统识别精度低的问题,构建最小模糊度优化模型并计算各特征信号的最优模糊隶属度函数,获取特征样本最大隶属度。构建截齿磨损程度的神经网络识别模型,运用多特征数据样本对Back-Propagation（BP）神经网络进行学习和训练。实验结果表明：BP神经网络识别模型的识别结果和试样的实际磨损程度类别相同,此识别模型能够对截齿磨损程度类型进行实时监测和准确识别。研究结果为实际工程中截齿监测和更换提供了解决方案。     

基于改进CRITIC法的TBM刀具声发射信号研究

针对地下施工中TBM(Tunnel Boring Machine)刀具磨损更换频繁且缺乏有效方法对其状态进行评估问题,将声发射技术用于TBM刀具检测,以TBM模态掘进试验台为对象,采集不同磨损程度的滚刀声发射信号研究声发射单特征参量及多特征参量对滚刀磨损状态趋势评估影响,提出基于改进CRITIC声发射多特征融合刀具状态评估新方法。滚刀磨损量测试表明,改进CRITIC声发射多特征融合后所得评估值对刀具磨损信息更敏感,能有效评估及预测刀具磨损状态,可为TBM刀具现场检修、保养提供指导。     

Prediction of cutting force based on machining parameters on AL7075-T6 aluminum alloy by response surface methodology in end milling

Cutting forces modeling is the basic to understand the cutting process, which should be kept in minimum to reduce tool deflection, vibration, tool wear and optimize the process parameters in order to obtain a high quality product within minimum machining time. In this paper a statistical model has been developed to predict cutting force in terms of geometrical parameters such as rake angle, nose radius of cutting tool and machining parameters such as cutting speed, cutting feed and axial depth of cut. Response surface methodology experimental design was employed for conducting experiments. The work piece material is Aluminum (Al 7075-T6) and the tool used is high speed steel end mill cutter with different tool geometry. The cutting forces are measured using three axis milling tool dynamometer. The second order mathematical model in terms of machining parameters is developed for predicting cutting forces. The adequacy of the model is checked by employing ANOVA. The direct effect of the process parameter with cutting forces are analyzed, which helps to select process parameter in order to keep cutting forces minimum, which ensures the stability of end milling process. The study observed that feed rate has the highest statistical and physical influence on cutting force.     

An effective sensor for tool wear monitoring in face milling: Acoustic emission

Acoustic Emission (AE) has been widely used for monitoring manufacturing processes particularly those involving metal cutting. Monitoring the condition of the cutting tool in the machining process is very important since tool condition will affect the part size, quality and an unexpected tool failure may damage the tool, work-piece and sometimes the machine tool itself. AE can be effectively used for tool condition monitoring applications because the emissions from process changes like tool wear, chip formation i.e. plastic deformation, etc. can be directly related to the mechanics of the process. Also AE can very effectively respond to changes like tool fracture, tool chipping, etc. when compared to cutting force and since the frequency range is much higher than that of machine vibrations and environmental noises, a relatively uncontaminated signal can be obtained. AE signal analysis was applied for sensing tool wear in face milling operations. Cutting tests were carried out on a vertical milling machine. Tests were carried out for a given cutting condition, using single insert, two inserts (adjacent and opposite) and three inserts in the cutter. AE signal parameters like ring down count and rms voltage were measured and were correlated with flank wear values (VB max). The results of this investigation indicate that AE can be effectively used for monitoring tool wear in face milling operations.     

开缝衬套成形过程声发射在线监测系统设计

为提高开缝衬套的双轴柔性滚弯成形质量,设计了一套基于声发射信号和虚拟仪器的在线监测系统,该系统主要由数据采集软件、数据采集卡、声发射传感器以及信号调理电路构成,实现对双轴柔性滚弯成形过程的声发射信号进行采集、显示、分析和存储,为开缝衬套双轴柔性滚弯成形过程控制和质量预测提供数据支持.通过实验对比分析了开缝衬套的双轴柔性滚弯过程声发射信号的时域和频域特性.实验结果表明,该在线监测系统响应速度快,性能稳定,能够可靠地实现对开缝衬套双轴柔性滚弯成形过程声发射信号的在线监测.     

New memory‐type dispersion control charts

The exponentially weighted moving average (EWMA) control chart is a memory‐type process monitoring tool that is frequently used to monitor small and moderate disturbances in the process mean and/or process dispersion. In this study, we propose 2 new memory‐type control charts for monitoring changes in the process dispersion, namely, the generally weighted moving average and the hybrid EWMA charts. We use Monte Carlo simulations to compute the run length profiles of the proposed control charts. The run length comparisons of the proposed and existing charts reveal that the generally weighted moving average and hybrid EWMA charts provide better protection than the existing EWMA chart when detecting small to moderate shifts in the process dispersion. An illustrative dataset is also used to show the superiority of the proposed charts over the existing chart.     

气体阀门内漏声发射评价参量研究及其应用

阀门是工业生产中必不可少的流体控制设备,阀门内漏往往不易发现和判定。近年来,声发射技术凭借其较高的灵敏度在阀门内漏检测中得到广泛应用。针对传统声发射参量——声发射均方根电压,在阀门内漏评价中表现出的抗干扰能力弱的特点,提出一个用于阀门内漏评价的新声发射参量——平均声发射能量。通过开展气体阀门内漏声发射检测实验,对新构建参量和传统评价参量评价效果进行对比,结果表明新参量评价精确度更高,阀门内漏率的估算误差低于10%,可满足工程应用的要求。     

Book review of Implementing Virtual Teams by A. Edwards and J.R. Wilson,Gower Publishing Ltd,Aldershot, 2004, pages xvi+190, £49.95, hardback (ISBN 0 566 08468 6)

Tool wear monitoring and estimation are essential for improved productivity of manufacturing systems. Multi-sensory approaches based on force, vibration and Acoustic Emission (AE) signals have been recognized as potential methods for tool wear monitoring. In the present work, steady-state components of force, dynamics of the main cutting force and vibration in the direction of the main cutting force have been used for on-line tool wear estimation in a turning process. The group method of data handling (GMDH), a heuristic self-organizing method of modelling, has been used to integrate information from different sensors and the cutting conditions to obtain estimates of tool wear. Different methods of preprocessing the forces have been attempted to determine the best method to suit the data. Various heuristics of GMDH have been analysed to obtain the appropriate models for tool wear estimation. The results show that GMDH can be effectively used to integrate sensor information and obtain reliable estimates of tool wear.     

ACOUSTIC EMISSION AND MACHINING - PROCESS ANALYSIS AND CONTROL

A quantitative model considering signal attenuation and tool flank wear is proposed to describe the influence of machining process parameters on the level of acoustic emission (AE) generation. The AE sensitivities to width of cut, feed rate, and hardness of material in three-dimensional cutting are experimentally studied. The application of AE signal analysis to chip formation detection, providing information for process control in unattended machining, is described.     

Statistical Monitoring of a Sealing Process by Means of Multivariate Accelerometer Data

A multivariate control charting procedure is applied to on-line seal quality evaluation of a packaging process by means of an accelerometer. Based on physical insight it is elucidated in a first step which information in the raw accelerometer data are relevant with respect to the goal of detecting bad seals. Next, a principal component analysis (PCA) based processing of this multivariate information is performed and the related Hotelling's T² and Q test statistics are calculated for further data representation. In a last step proper control charts based on these statistics are used as a process monitoring tool for on-line distinction between good and bad seals. The obtained results show that a correct monitoring of accelerometer signals can be a useful tool for the on-line detection of 'bad seals' in a packaging process.     

刀具磨损声发射信号小波分析中小波基的选取

针对在用小波理论分析刀具磨损声发射（AE）信号时选取不同的小波基对分析结果有重要影响的问题,通过对小波基性质和刀具磨损AE信号特点的研究,从理论上分析了小波分析中刀具磨损AE信号处理中小波基选取的方法。在试验验证过程中,根据信号在小波包分解前后遵循能量守恒的原理,用四种小波基对刀具磨损AE信号进行三层小波包分解。以经小波包分解后AE信号各频带上的频带能量为特征参数,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。     

New EWMA control charts for monitoring process dispersion using auxiliary information

The exponentially weighted moving average (EWMA) control chart is a well‐known statistical process monitoring tool because of its exceptional pace in catching infrequent variations in the process parameter(s). In this paper, we propose new EWMA charts using the auxiliary information for efficiently monitoring the process dispersion, named the auxiliary‐information–based (AIB) EWMA (AIB‐EWMA) charts. These AIB‐EWMA charts are based on the regression estimators that require information on the quality characteristic under study as well as on any related auxiliary characteristic. Extensive Monte Carlo simulation are used to compute and study the run length profiles of the AIB‐EWMA charts. The proposed charts are comprehensively compared with a recent powerful EWMA chart—which has been shown to be better than the existing EWMA charts—and an existing AIB‐Shewhart chart. It turns out that the proposed charts perform uniformly better than the existing charts. An illustrative example is also given to explain the implementation and working of the AIB‐EWMA charts.