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.     

Spindle with built-in acoustic emission sensor to realize contact detection

Recently, the demand for realizing micromachining through small-diameter tools has increased. When performing microfabrication using a numerically controlled machine tool, a machining error may be introduced if the relative position of the tool tip and workpiece surface deviates during tool change. Therefore, it is critical to determine this relative position in an actual machining condition at a specific spindle speed. We are currently developing an air bearing turbine spindle with a built-in acoustic emission sensor that can detect the contact of the tool tip with the workpiece surface in real time. The acoustic emission (AE) signal generated at the tool tip can be accurately detected by placing the AE sensor in direct contact with the tool end surface inside the main shaft floated by air. In this study, we investigated the possibility of contact detection between the tool tip and the workpiece surface at the submicrometer level through the proposed spindle. The results of the performed evaluation experiments indicated that by using the spindle with a built-in acoustic emission sensor, the contact of the small-diameter tool tip with the workpiece surface could be detected with damage to the workpiece at the submicrometer level on average.     

磨削过程知识学习

磨削加工光磨时间、加工节拍直接体现磨削加工参数,粗磨过程的好坏是磨削过程的关键,磨削过程声发射信号粗磨段上升部分包含着磨削过程最丰富的信息,采用平均三角分配模糊规则对磨削过程声发射信号粗磨段上升部分进行知识获取和自学习,建立磨削加工光磨时间、加工节拍与磨削声发射曲线粗磨段上升部分斜率之间的对应关系,据此可得到任意光磨时间、加工节拍时对应的磨削声发射曲线粗磨段上升部分斜率。以此判断加工参数选择的合理性,以实现磨削加工的加工参数自动选择和智能控制,确保加工质量,实现磨削过程加工参数在线调整、磨削智能化。     

Linear discriminant function analysis of acoustic emission signals for cutting tool monitoring

A principal setback to automation of the machining process is the inability to completely monitor the condition of the cutting tool in real time. Whereas several of the techniques developed to date are useful in specific applications, no universally applicable sensor is yet available.Acoustic emission is one of the most promising techniques to be recently developed for on-line cutting tool monitoring. However, signal analysis is still an area that requires further investigation to enhance the potential of acoustic emission. For this purpose, frequency-based pattern recognition concepts using linear discriminant functions have been used in analysing acoustic emission signals generated during machining to distinguish between different signal sources, specifically chip formation, tool fracture, and chip noise. Five features were used for classification in the frequency range of 100 kHz to 1 MHz, with each feature consisting of a 20 kHz bandwidth, and were selected using the class mean scatter criterion. The coefficients of the discriminant functions were obtained by training the system using signals generated by each of the sources of interest. An AISI 1018 steel was machined using a titanium carbide-coated cutting tool. Cutting speeds ranged from 200 to 800 ft/min (1 to 4 m/sec) with feed rats of 0·0005 to 0·0075 in/rev (0·0133 mm/rev to 0·191 mm/rev) and depth of cut 0·17 in (4·32 mm). The results show a successful classification rate of 90% for tool breakage, while those for chip formation and chip noise were 97 and 86% respectively.     

A neural network based approach for background noise reduction in airborne acoustic emission of a machining process

Tool wear prediction has become an indispensable technique to prevent downtime in manufacturing and production processes. Airborne emission from a machining process using a low-cost microphone may provide a vital signal of tool health. However, the effect of background noise results in anomaly in data that may lead to wrong prediction of tool health. The paper presents an adaptive approach using neural networks for background noise filtration in acoustic signal for a turning process. Acoustic signal of a turning process is mixed with background noise from four different machines and introduced at different RPMs and feed-rate at a constant depth of cut. A comparison of Backpropagation neural network (BPNN), Self-organizing map and k-means clustering algorithm for noise filtration is investigated in this paper. In this regard, back-propagation neural network showed better performance with an average accuracy for all the four sources. It shows 100 % accuracy for grinding machine signal, 94.78 % accuracy for background signal from 3-axis milling machine, 45.57 % and 12.69 % for motor and 4-axis milling machine, respectively. Signal reconstruction is then done using Discrete cosine transform (DCT). The proposed technique shows a promising future for noise filtration in airborne acoustic data of a machining process.     

Self-Learning Algorithm for Automated Design of Condition Monitoring Systems for Milling Operations

This paper investigates an approach, termed self-learning ASPS (automated sensor and signal processing selection), aimed at aiding the systematic design of condition monitoring systems for machining operations. The paper outlines a self-learning methodology for the classification of the system’s normal and faulty states and the selection of the most appropriate sensors and signal processing methods for detecting machining faults in end milling. The aim of the proposed approach is to enable the condition monitoring designer to use previous system faults or incidents to design an on-line monitoring system, reducing the system’s development time and cost. Force, acceleration and acoustic emission signals are used to design the condition monitoring systems for end milling operations. Gradual tool wear, catastrophic cutter breakage and tool collision are used for evaluating the proposed self-learning ASPS approach. The initial results show that the suggested algorithm can be applied for an automated, self-learning monitoring system for the selection of the most sensitive sensors and signal processing methods for machining faults and conditions.       

基于圆台形热传导模型的慢走丝线切割仿真与试验

基于放电通道中等离子体的形成机理,根据慢走丝线切割在短脉冲放电加工时放电通道中电子流与离子流散射速度的差异,提出了圆台形热传导模型。采用基于圆台形热传导模型的有限单元法对航空材料Inconel 718的典型工况进行了仿真计算,系统地分析了放电能量对放电通道温度以及放电蚀坑深度的影响规律,并采用声发射检测技术在线监测慢走丝线切割的加工表面粗糙度。通过仿真结果与试验测得工件表面粗糙度Rt值的对比,再结合试验测得的声发射信号波形图特征及声发射信号均方根值发现：仿真计算得到的放电蚀坑深度与表面粗糙度Rt值吻合较好;声发射信号的强度随着放电能量的增加而增强,声发射信号强度随着放电温度变化速率的变小而减弱。最后回归分析得到材料表面粗糙度与声发射信号均方根值的数学预测模型,预测结果与测得的表面粗糙度误差仅为4.4%。     

基于B样条模糊神经网络的刀具磨损监测

刀具状态监测是实现自动化加工和无人化加工的关键技术。本文使用切削力和声发射传感器监测金属切削过程,提出了基于B样条模糊神经网络作为刀具磨损量监测模型。该模型能够准确描述刀具磨损和信号特征之间的非线性关系,和常用的BP前馈神经网络相比,具有收敛速度快和局部学习能力等优点。试验结果表明:采用B样条模糊神经网络对提高刀具磨损在线监测的准确度和可靠度非常有效。     

A new approach to investigate tool condition using dummy tool holder and sensor setup

The industrial demand for automated machining systems to enhance process productivity and quality in machining aerospace components requires investigation of tool condition monitoring. The formation of chip and its removal have a remarkable effect on the state of the cutting tool during turning. This work presents a new technique using acoustic emission (AE) to monitor the tool condition by separating the chip formation frequencies from the rest of the signal which comes mostly from tool wear and plastic deformation of the work material. A dummy tool holder and sensor setup have been designed and integrated with the conventional tool holder system to capture the time-domain chip formation signals independently during turning. Several dry turning tests have been conducted at the speed ranging from 120 to 180?m/min, feed rate from 0.20 to 0.50?mm/rev, and depth of cut from 1 to 1.5?mm. The tool insert used was TiN-coated carbide while the work material was high-carbon steel. The signals from the dummy setup clearly differ from the AE signals of the conventional setup. It has been observed that time-domain signal and corresponding frequency response can predict the tool conditions. The rate of tool wear was found to decrease with chip breakage even at higher feed rate. The tool wear and plastic deformation were viewed to decrease with the increased radius of chip curvature and thinner chip thickness even at the highest cutting speed, and these have been verified by measuring tool wear. The chip formation frequency has been found to be within 97.7 to 640?kHz.     

磨削粗糙度在线检测方法的实验研究

本文对基于摩擦声发射信号的磨削粗糙度在线检测方法进行了实验研究。实验结果表明，采用声发射传感器探头与磨削表面摩擦产生的声发射信号的特征可以对磨削表面粗糙度进行评价，建立了摩擦声发射信号特征与磨削表面粗糙度之间的对应关系，并通过实验对该方法的可行性进行了实测。结果表明，探针与工件表面摩擦声发射信号的FFT和RMS特征与磨削粗糙度有很好的对应关系，可用于磨削表面粗糙度的在线检测。     

Monitoring of Ball Bearing Operation under Grease Lubrication Using a New Compound Diagnostic System Detecting Vibration and Acoustic Emission

Our new compound diagnostic system comprised of a compound sensor, a signal processor, and a personal computer installed signal processing software. The compound sensor made by an advanced sensor fusion technique was able to detect simultaneously the vibration acceleration and the acoustic emission by itself. The signal processor received a signal from the sensor and separated it into the vibration acceleration signal and the acoustic emission signal. The signal processor and the personal computer processed the acceleration signal and acoustic emission signal for diagnostic information. The rolling contact fatigue process of a ball bearing under grease lubrication was monitored using the compound system. The system outputs diagnostic information, for example, the means, the variance, the skewness, and the kurtosis of the vibration acceleration signal and the acoustic emission signal. In diagnosing the rolling contact fatigue failure, the root mean square (rms) value of the vibration acceleration was most effective, and the mean of the demodulated acoustic emission was second to the rms value of the acceleration in effectiveness. From the result of the evaluation, it became clear that the system was useful for diagnosing rolling contact bearings under grease lubrication.     

Machining Characteristics of Micro-Flow Channels in Micro-Milling Process

□ This article presents a method for machining micro-flow channels on dies for precise and effective mass production of metallic bipolar plates of proton exchange membrane fuel cells (PEMFCs). To find an effective method for machining micro-flow channels on dies of metallic bipolar plates, machining experiments are conducted on micro-flow channels using a micro-scale milling process under various machining conditions. Machining variables are axial depth of cut, feed per tooth, supply/non-supply of cold wind, and single-tool/multi-tool cutting processes. The machining process is monitored by acquiring cutting force signals and acoustic emission signals while conducting the experiments. Surface conditions of machined micro-flow channels are analyzed. In this study, a cold-wind process is better than other processes in terms of burr generation and surface roughness. A cold-wind process with multi-tool process has a particularly better surface quality than the other processes.     

A composite fitting model of discrete handbook data for peripheral end milling

Machining data handbooks are important reference books in the machining industry, as they provide recommended process parameter values for common machining operations. The machining data, although covering a wide range of relevant cutting conditions, are only listed under discrete cutting conditions. Rough interpolation-based calculations are often needed in order to estimate the process parameter values at the desired cutting condition. In this work, a composite fitting model is presented to fit a composite functional curve through the discrete handbook data of recommended cutting speeds and feeds with respect to the cutting condition of radial depth of cut for peripheral end milling. The objective is to establish a functional relationship from the handbook data such that recommended cutting speed and feed can be obtained for any given radial depth of cut. According to the tabulated layout of the machining data, the entire range of the radial depth of cut is divided into three segments having distinctive formulations and trends. Constraints are then imposed to preserve the trends and smoothly connect the adjacent segments. As a possible application of the presented model, a case study of machining a rectangular pocket is provided. Machining time of a potential process plan is readily evaluated based on the cutting speeds and feeds obtained from the composite model.     

基于模态分析和小波变换的声发射源定位新算法研究

针对传统声发射源定位中，声发射信号到达传感器的时间受设定门槛电压影响很大，导致声发射源定位效果较差，提出了一种声发射源定位新方法。根据模态声发射理论，携带声发射源信息的声发射信号在结构中传播过程中，具有频散现象和多模态特性。因此，声发射源定位应基于同一频率下、同一模态导波到达各个传感器的时间和传播速度。通过对声发射信号进行Gabor小波变换的方法，在时频空间内确定某一频率下某一模态导波到达传感器的时间；并通过数值计算得到该频率处模态导波的群速度，从而实现声发射源的准确定位。通过薄板中声发射线源定位试验，证明了该定位算法的有效性。     

新型PVDF声发射传感器的设计方法及应用

基于声发射技术研制了一种新型PVDF压电传感器。介绍了声发射传感器的结构、工作原理以及声发射传感器压电元件、背衬材料、声匹配层和前置放大器接口电路设计对传感器灵敏度、分辨率和信噪比的影响。利用美国物理声学公司声发射信号数据采集处理DiSP系统进行了断铅信号采集试验。测试结果表明：设计的新型PVDF声发射传感器对断铅信号具有宽频带响应。应用研制的新型PVDF声发射传感器、数字示波器和计算机组成的数据采集处理系统对4M20氮氢气压缩机六段排气阀进行了状态监测，结果表明新型声发射传感器能够用于设备状态监测，设计方法可行。     

环形腔全光纤F-P干涉仪的声发射检测实验研究

提出了一种新型结构的用于声发射检测的全光纤F-P干涉仪。选用2×2光纤耦合器,将耦合器的一个入射端与一个出射端焊接相连,以耦合器代替传统的反射腔面,构成光纤环形传输腔,腔体贴附或埋入待测固体中检测声发射信号。通过理论推导和计算机仿真,确定了此结构光纤传感器的检测特性。实验以大理石板作为待测介质,对利用信号发生器驱动PZT（压电陶瓷）作为已知超声源在大理石板中产生的连续型声发射信号,及冲击波作用下大理石板中产生的突发型声发射信号进行了检测,并利用Fourier变换,得到了声发射信号的特征频率。实验结果表明,此种结构传感器能够检测材料结构中促使光纤轴向伸缩长度的量级为10-8m的声发射信号并识别其特征频率,该结构光纤传感器无需光程的匹配,适用于大尺度构件的监测,为材料结构健康检测与监控提供了一种新的方法。     

MAXIMIZATION OF CHATTER-FREE MATERIAL REMOVAL RATE IN END MILLING USING ANALYTICAL METHODS

Chatter vibrations in milling, which develop due to dynamic interactions between the cutting tool and the workpiece, result in reduced productivity and part quality. Various numerical and analytical stability models have been considered in the previous publications, where mostly the stability limit of axial depth of cut is emphasized for chatter-free cutting. In this paper an analytical stability model is used, and a simple algorithm to determine the stability limit of radial depth of cut is presented. It is shown that, for the maximization of chatter-free material removal rate, radial depth of cut is of equal importance with the former. A method is proposed to determine the optimal combination of depths of cut, so that chatter-free material removal rate is maximized. The application of the method is demonstrated on a pocketing example where significant reduction in the machining time is obtained using the optimal parameters. The procedure can easily be integrated to a CAD/CAM or virtual machining environment in order to identify the optimal milling conditions automatically.     

MAXIMIZATION OF CHATTER-FREE MATERIAL REMOVAL RATE IN END MILLING USING ANALYTICAL METHODS

ABSTRACT

Chatter vibrations in milling, which develop due to dynamic interactions between the cutting tool and the workpiece, result in reduced productivity and part quality. Various numerical and analytical stability models have been considered in the previous publications, where mostly the stability limit of axial depth of cut is emphasized for chatter-free cutting. In this paper an analytical stability model is used, and a simple algorithm to determine the stability limit of radial depth of cut is presented. It is shown that, for the maximization of chatter-free material removal rate, radial depth of cut is of equal importance with the former. A method is proposed to determine the optimal combination of depths of cut, so that chatter-free material removal rate is maximized. The application of the method is demonstrated on a pocketing example where significant reduction in the machining time is obtained using the optimal parameters. The procedure can easily be integrated to a CAD/CAM or virtual machining environment in order to identify the optimal milling conditions automatically.     

基于Lab VIEW的声发射信号小波阈值去噪研究

虚拟仪器代表着目前测试仪器领域的发展方向，LabVIEW语言是一种功能强大的仪器开发平台。对淹没在噪声中声发射信号的有效提取（去噪）是声发射信号处理技术的第一步，也是声发射信号处理的关键所在。本文介绍了基于小波变换的阈值去噪方法。在LabVIEW平台上，通过仿真试验，对声发射信号的几种阈值法的去噪结果进行比较，选出一种适合声发射信号去噪的阈值准则。     

Design of multisensor fusion-based tool condition monitoring system in end milling

Recent advancement in signal processing and information technology has resulted in the use of multiple sensors for the effective monitoring of tool conditions, which is the most crucial feedback information to the process controller. Interestingly, the abundance of data collected from multiple sensors allows us to employ various techniques such as feature extraction, selection, and classification methods for generating such crucial information. While the use of multiple sensors has improved the accuracy in the classification of tool conditions, design of tool condition monitoring system (TCM) for reduced complexity and increased robustness has been rarely studied. Therefore, this paper studies the design of effective multisensor-based TCM when machining 4340 steel by using a multilayer-coated and multiflute carbide end mill cutter. Multiple sensors tested in this paper include force, vibration, acoustic emission, and spindle power sensor for the time and frequency domain data. In addition, two feature selection methods and three classifiers with a machine ensemble technique are considered as design components. Importantly, different fusion methods are evaluated in this paper: (1) decision level fusion and (2) feature level fusion. The experimental results show that the design of TCM based on the feature level fusion can significantly improve the accuracy of the tool condition classification. It is also shown that the highest accuracy can be achieved by using force, vibration, and acoustic emission sensor together with correlation-based feature selection method and majority voting machine ensemble.