基于声发射的铣刀破损信号特征的提取与优化

铣刀破损监测对实现加工自动化具有重要的意义.提出了基于小波变换的铣刀声发射破损特征提取与优化方法.首先,采用小波变换对铣刀声发射信号进行多分辨率分解,然后提取各频段子信号的能量比作为刀具破损监测的特征量.通过对正常切削、随机冲击和刀具破损这三类信号的比较分析,证明了该特征提取方法能够有效地反映刀具状态.最后,通过正交统计,分析了切削速度、进给速度和切削深度对特征量的影响,并对特征量进行优化.     

声发射在线监测刀具破损关键技术的研究

声发射(AE)法是被公认的较为理想的在线监测刀具破损的方法,但该方法要完全适于加工中心乃至自动线的需要,还有许多关键性技术问题需要解决。本文对三个关键性技术问题,即对声发射检测仪理想滤波器的设计方法、钻铣类加工机床刀具破损AE信号传输的新途径—磁流体传导技术、刀具破损声发射信号时频特性的确定进行了研究。     

基于语音识别技术的刀具工况在线监测的研究

分析了刀具在线监测研究概况,提出了基于语音识别技术的刀具工况在线监测方法.实验结果表明,刀具切削声谱特征与其磨损情况之间具有对应关系,刀具磨损的工况信息与所产生的声信号具有同步效应,可以实时地获得监测信息,预先发现故障或危险.为刀具工况监测提供一种新的有效方法.     

刀具破损的在线监测

刀具破损的在线监测,是自动化加工中的重要问题。本文概述了刀具破损的在线监测技术的发展概况,对现用的监测方法进行了分类和评述,并对主要的典型监测方法如:光学摄像法、切削力法、声发射法、电机功耗法、振动信号法等进行了具体介绍。图11幅、表1个。     

数控镗刀磨损与破损的声发射监测法

通过分析声发射传感器采集的刀具磨损状态信号,提取出反映刀具磨损状态的特征向量MFCC系数及差分系数,然后利用隐马尔可夫模型进行信号处理,建立了检测镗刀刀具状态的监测系统。实验结果表明:在刀具的正常磨损阶段,该监测系统可以实现刀具大致磨损量的预报;在刀具破损或损坏情况下,能够及时监测和预报刀具损坏状态。这种监测方法可用于实时在线监测,为刀具的磨损监测提供了一条切实可行的途径。     

数控机床加工曲面时刀具破损的小波分析

介绍小波变换思想及特点，根据傅里叶变换原理，结合刀具破损信号，分析了快速小波变换－Mallat算法。实验表明多分辨分析的方法，对于刀具破损突变信号具有精确时－频定位和易于监测的优点，能够有效处理刀具破损监控的信号。     

基于声发射信号的振动钻削刀具磨损状态监测试验研究

针对钻削加工时难以直接观察刀具磨损状态的问题,基于声发射采集系统设计了超声轴向振动钻削刀具磨损状态监测装置,并在7075铝板上进行超声振动钻削试验。分析刀具磨损状态对声发射信号RMS值的影响,并通过小波分解技术对比分析刀具在不同磨损状态下的声发射信号变化规律;根据声发射信号对刀具磨损状态进行实时监测。试验结果表明:声发射信号的RMS值与刀具的磨损程度呈正相关;通过小波分解可知,随着刀具磨损的增加,信号的能量逐渐由低频段向高频段转移,可以通过监测声发射信号RMS值与能量的变化实现刀具磨损状态的有效识别。     

铣刀破损的声发射检测技术

用声发射技术检测铣刀破损是一项新技术。文章叙述了应用信号处理和计算机技术对铣削加工中刀具破损的声发射信号进行时频分析,进而提出在频率要350～500kHz之间检测刀具破损的方法,并介绍了由此建立的铣刀破损检测系统。图5幅。     

加工中心刀具破损实时监控技术的研究

在刀具破损监控中监控策略及采用何种特征量是实现实时监控的关键,常规单一的监控方法其适应性有限且容易漏监错监.文章详细分析了加工过程的声发射、切削力及功率变化特点并结合加工中心的加工特点,提出了其于双阈值的声发射监控、切削力遥测监控和基于功率微分的多特征参数综合刀具破损检测技术,能针对不同的加工即粗、精加工同时给与监测,且受切削条件的影响小,具有监测精度高、抗干扰能力强、灵敏度高、可以实时检测等特点.     

基于EEMD和IMF能量分布的刀具破损识别

针对铣削过程中声发射信号非平稳的特点,提出了一种基于噪声辅助经验模态分解(EEMD)和本征模函数(IMF)能量分布的刀具破损识别方法.首先对经过滤波后的原信号进行EEMD分解,抽取本征模函数组(IMF),后计算每一阶模函数能量及总体能量分布,最后提取特征向量,通过特征向量的变化识别刀具破损.利用该方法,在立式铣削加工中心上对稳定切削中刀具破损和变参数铣削加工进行了系统的分析,结果表明此方法能够剔除切削参数变化的影响,准确的识别刀具破损,具有很高的稳定性和准确性.     

Micro-end-milling-III. Wear estimation and tool breakage detection using acoustic emission signals

Acoustic Emission (AE) signals have been used to monitor tool condition in conventional machining operations. In this paper, new procedures are proposed to detect tool breakage and to estimate tool condition (wear) by using AE. The proposed procedure filters the AE signals with a narrow band-width, band-pass filter and obtains the upper envelope of the harmonic signal by using analog hardware. The envelope is digitized, encoded and classified to monitor the machining operation. The characteristics of the envelope of the AE were evaluated to detect tool breakage. The encoded parameters of the envelope of the AE signals were classified by using the Adaptive Resonance Theory (ART2) and Abductory Induction Mechanism (AIM) to estimate wear. The proposed tool breakage and wear estimation techniques were tested on the experimental data. Both methods were found to be acceptable. However, the reliability of the tool breakage detection system was higher than the wear estimation method.     

Complexity measure of motor current signals for tool flute breakage detection in end milling

Automated tool condition monitoring is an important issue in the advanced machining process. Permutation entropy of a time series is a simple, robust and extremely fast complexity measure method for distinguishing the different conditions of a physical system. In this study, the permutation entropy of feed-motor current signals in end milling was applied to detect tool breakage. The detection method is composed of the estimation of permutation entropy and wavelet-based de-noising. To confirm the effectiveness and robustness of the method, typical experiments have been performed from the cutter runout and entry/exit cuts to cutting parameters variation. Results showed that the new method could successfully extract significant signature from the feed-motor current signals to effectively detect tool flute breakage during end milling. Whilst, this detection method was based on current sensors, so it possesses excellent potential for practical and real-time application at a low cost by comparison with the alternative sensors.     

The shape characteristic detection of tool breakage in milling operations

Detection of tool failure is very important in automated manufacturing. All previously developed tool breakage detection approaches in milling operations have adopted the strategy of parameter detection in which the detection of tool breakage was carried out according to values of specific parameters selected to reflect tool state (with or without tool breakage). In this paper the new concept of shape characteristic detection of tool breakage in milling operations is proposed. The detection of tool breakage is conducted according to the shape characteristics of discrete dyadic wavelet decomposition of cutting force. By means of the proposed method, the influence caused by the variation of cutting parameters and transients is eliminated. The proposed method is conducted in two steps. In the first step, cutting force signals are decomposed by discrete dyadic wavelet, with the shape characteristic vectors then being generated by the proposed shape characteristic vector-generating algorithm. In the second step, the shape characteristic vectors are fast classified by the ART2 neural networks. The accuracy and effectiveness of the proposed method are verified by numerous experiments.     

Flute breakage detection during end milling using Hilbert–Huang transform and smoothed nonlinear energy operator

Real time tool condition monitoring has great significance in modern manufacturing processes. In order to prevent possible damages to the workpiece or the machine tool, reliable monitoring techniques are required to provide fast response to the unexpected tool failure. Milling is one of the most fundamental machining operations. During the milling process, the current of feed motor is weakly related to the cutter condition, the change of power consumption is not significant to identify tool condition. Thus, current of motor-based tool condition still requires some new approaches to sort out significant pattern that could be employed to indicate tool condition. In this paper, a new approach is proposed to detect end mill flute breakage via the feed-motor current signals, which implements Hilbert–Huang transform (HHT) analysis and a smoothed nonlinear energy operator (SNEO) to extract the crucial characteristics from the measured signals to indicate tool breakage. Experiments on a CNC Vertical Machining Centre are presented to show the algorithm performance. The results show that this method is feasible and can accurately and efficiently monitor the conditions of the end mill under varying cutting conditions.     

Real time monitoring of tool breakage in a milling operation using a digital signal processor

A monitoring system that can detect tool breakage and chipping in real time was developed using a digital signal processor (DSP) board in a face milling operation. An autoregressive (AR) model and a band energy method were used to extract the features of tool states from cutting force signals. Then, two artificial neural networks, which have a parallel processing capability, were embedded on the DSP board to discriminate different malfunction states from features obtained by each of the two methods of signal processing. In experiments, we found that feature parameters extracted by AR modeling were more accurate indicators of malfunctions in the process than those from the band energy method, although the computing speed is slower. By using the selected features, we were able to monitor malfunctions in real time.     

End milling tool breakage detection using lifting scheme and Mahalanobis distance

In this paper, a novel method based on lifting scheme and Mahalanobis distance (MD) is proposed for detection of tool breakage via acoustic emission (AE) signals generated in end milling process. The method consists of three stages. First, by investigating the specialty of AE signals, a biorthogonal wavelet with impact property is constructed using lifting scheme, and wavelet transform is carried out to separate AE components from the original signals. Second, Hilbert transform is adopted to demodulate signal envelope on wavelet coefficients and salient features indicating the tool state (i.e., normal conditions, slight breakage, and serious breakage) are extracted. Finally, tool conditions are identified directly through the recognition of these features by means of MD. Practical application results on a CNC vertical milling machine tool show that the proposed method is accurate for feature extraction and efficient for condition monitoring of cutting tools in end milling process.     

Approach into the use of probabilistic neural networks for automated classification of tool malfunctions in broaching

The condition of broaching tools has crucial importance for the surface quality of the machined components. If undetected, tool malfunctions such as wear, chipping and breakage of cutting teeth can result in severe damage or even scrapping expensive components, with direct implications on increasing the overall manufacturing costs. In contrast with other machining operations, broaching is characterised by non-symmetric distributions of cutting forces vs. time, making more difficult the task of recognising tool malfunctions. The paper reports on a methodology to automatically detect and classify tool malfunctions in broaching. The method was demonstrated through the use of time domain distribution of the push-off cutting force as a key sensory signal to monitor broaching tool condition when machining a nickel-based aerospace alloy. The characteristic features of the sensory signals have been extracted using in-house-developed programs and, afterwards, used to train and test a probabilistic neural network that enables automated classification of tools with fresh, worn, chipped and broken teeth. Inputting new pattern characteristics to the main categories of tool malfunctions, the system successfully classified them even when variations of signal amplitude and ranking of malfunctioned teeth occurred.     

Discrete wavelet transform for tool breakage monitoring

Detection of tool breakage is of vital importance in automated manufacturing. Various methods have been attempted, and it is considered that the use of discrete wavelet transform (DWT), which is much more efficient and just as accurate wavelet analysis, may provide a realistic solution to the detection of tool breakage in operation. The DWT uses an analyzing wavelet function which is localized in both time and frequency to detect a small change in the input signals. In addition, it requires less computation than Fast Fourier Transformation (FFT). This paper discusses a tool breakage monitoring system based on DWT of an acoustic emission (AE) and an electric feed current signal using an effective algorithm. The experiment results show overall 98.5% reliability and the good real-time monitoring capability of the proposed methodology for detecting tool breakage during drilling.     

基于声信号HMM的刀具磨损程度分级识别

为有效地实时在线监测刀具的磨损状态，提出了基于声音识别技术的刀具磨损监测方法，进行了基于切削声信号HMM的刀具磨损程度的分级识别，监测系统能够对刀具的五级磨损划分进行准确识别，这为刀具的磨损监测提供了一条切实可行的途径。