一种新型高灵敏度双叠片式流体声发射传感器的研制

研制高灵敏度、安装使用方便、抗干扰能力强的传感器是实现刀具磨损在线监测的关键.参考空气声学中常用的压差式微音器的典型结构,考虑自动化加工中使用的要求,研制成功可用于刀具磨损状态监测的非接触高灵敏度双叠片式流体声发射传感器.对研制的流体声发射传感器性能进行了实验分析,结果表明传感器对刀具磨损产生的声发射信号具有较高的灵敏度.     

流体声发射传感器实验性能研究

在专用实验台和测试系统上，对流体声发射传感器进行了水流体、油流体和空气流体及流体温度变化时的多种实验，给出了实验性能曲线，对影响其传播性能的各种因素进行了讨论，针对金属表面摩擦磨损进行了实验研究，给出了此流体声发射传感器传播的经验模型，为正确利用此流体声发射传感器奠定了实验基础，     

小波分析在数控刀具磨损状态监测中的应用

研究了数控刀具切削过程中声发射(AE)信号的产生机理和特点,提出了利用小波分解和小波包分解技术提取AE信号特征参数的方法监测刀具的磨损状态,并通过实例验证了该方法在刀具磨损监测中的可行性.     

单晶硅研磨过程的声发射在线监测研究

本文在BIN62型超精密研抛机的基础上设计了研磨过程的声发射在线监测装置,试验研究了不同研磨工况对声发射信号RMS值和材料去除率的影响规律,采用回归分析方法建立了材料去除率与声发射信号RMS值的线性数学模型,并通过声发射波形的频谱分析和表面形貌的观测研究了单晶硅研磨过程中的声发射源机制。结果表明：在保持其他研磨工况不变的条件下,声发射信号RMS值随着研磨压力或研磨速度的增加而增加;根据RMS值可实现材料去除率的在线监测,在给定研磨工况范围内材料去除率预测模型的预测误差小于4.2%;声发射波形的频谱分析技术可用于声发射源机制的识别,单晶硅研磨过程中声发射信号主要的频率成分出现在50 kHz~260 kHz频段内,声发射信号主要来源于材料的脆性解理、磨粒磨损和轻微粘结磨损。     

滑坡体临滑声发射监测主机的设计

基于滑坡体临滑时因岩石断裂、岩石摩擦而产生声发射的特点,提出一种基于声发射监测的滑坡体临滑监测主机的设计方案。系统能监测的声发射频率范围为1Hz～20kHz,能监测的最小声压为0.002Pa。通过安装在滑坡体附近的声发射传感器,连续监测和记录滑坡体临滑时产生的声发射,可用于分析其变化规律和特征,从而实现对滑坡灾害的预测预报。     

基于多传感器的刀具状态监测系统

以铣削加工为对象,研究了多刃切削加工过程的刀具状态监测问题,从系统的角度分析了刀具状态的多传感器监测原理,并以此为依据确定了采用声发射（ＡＥ）传感器和动态切削力传感器可有效地监测加工过程。文中提出了一种多传感器信号的特征提取方法,该方法利用偏最小二乘法计算样本矩阵的本征值,根据置信因子确定特征维数。为验证该方法的有效性,建立了一个铣削加工实验系统,实验结果表明,该方法可在多种切削条件下获得较高的识别率。     

基于回归树和AdaBoost方法的刀具磨损评估

本文利用高速数控铣刀铣削中不同侧面方向的切削力和振动信号以及声发射信号均方根值,以数据驱动的形式对刀具磨损进行了拟合评估. 在本次研究中,分别从时域、频域和时频联合域上探索与刀具磨损相关的敏感特征,具体特征提取方法包括时域统计分析、频域上的快速傅里叶变换（FFT）和时频联合分析的小波变换（WT）. 本文中,决策树被用于回归问题而非分类问题,用于评估刀具磨损值. 同时,引入AdaBoost算法对回归树模型进行提升,并从模型的准确性、稳定性和适用性三个方面上综合对比了提升的决策树回归模型和原模型的性能. 研究表明,AdaBoost算法提升的回归决策树模型在预测的准确性和稳定性上都有一定程度上提高,并且在面向全新刀具磨损预测的适用性上也取得了不错的提升效果.     

小波包分析在刀具声发射信号特征提取中的应用

分析了刀具的切削状态,介绍了刀具的声发射信号检测系统和小波、小波包分析技术,以及小波包频带能量分解方法,提出了小波包分解功率监测特征量提取技术.通过在刀具声发射的一个实例信号中的应用,有效地区分了刀具的两种切削状态,验证了小波包分解功率监测特征量提取方法的可行性.     

非接触式光纤声发射传感器的设计

为克服常用声发射传感器必须和被测物体接触、工作频带较窄且带内幅频特性波动较大和易受电磁干扰的不足,提出采用光纤干涉仪原理研制声发射传感器.介绍了光纤声发射传感器的原理、系统组成.其频率范围为100kHz～1.4MHz,振幅分辨率为0.08nm.     

声表面波NO_2传感器敏感膜研究进展

由于工业检测、环境监测、医学监测等领域的需求,高性能NO2传感器得到了广泛的研究。声表面波传感器技术的发展为研发高灵敏度、高稳定性、响应快速、小型化的NO2传感器提供了极大的潜能。总结了近30年来声表面波NO2传感器敏感膜的研究现状,并根据现有的研究和传感器的应用需求,深入探讨了声表面波NO2传感器敏感膜面临的挑战和发展趋势。     

On-line tool condition monitoring system with wavelet fuzzy neural network

In manufacturing systems such as flexible manufacturing systems (FMS), one of the most important issues is accurate detection of the tool conditions under given cutting conditions. An investigation is presented of a tool condition monitoring system (TCMS), which consists of a wavelet transform preprocessor for generating features from acoustic emission (AE) signals, followed by a high speed neural network with fuzzy inference for associating the preprocessor outputs with the appropriate decisions. A wavelet transform can decompose AE signals into different frequency bands in the time domain. The root mean square (RMS) values extracted from the decomposed signal for each frequency band were used as the monitoring feature. A fuzzy neural network (FNN) is proposed to describe the relationship between the tool conditions and the monitoring features; this requires less computation than a back propagation neural network (BPNN). The experimental results indicate the monitoring features have a low sensitivity to changes of the cutting conditions and FNN has a high monitoring success rate in a wide range of cutting conditions; TCMS with a wavelet fuzzy neural network is feasible.     

Multilevel classification of milling tool wear with confidence estimation

An important problem during industrial machining operations is the detection and classification of tool wear. Past research in this area has demonstrated the effectiveness of various feature sets and binary classifiers. Here, the goal is to develop a classifier which makes use of the dynamic characteristics of tool wear in a metal milling application and which replaces the standard binary classification result with two outputs: a prediction of the wear level (quantized) and a gradient measure that is the posterior probability (or confidence) that the tool is worn given the observed feature sequence. The classifier tracks the dynamics of sensor data within a single cutting pass as well as the evolution of wear from sharp to dull. Different alternatives to parameter estimation with sparsely-labeled training data are proposed and evaluated. We achieve high accuracy across changing cutting conditions, even with a limited feature set drawn from a single sensor.     

Comparing orthogonal force and unidirectional strain component processing for tool condition monitoring

Signal processing using orthogonal cutting force components for tool condition monitoring has established itself in literature. In the application of single axis strain sensors however a linear combination of cutting force components has to be processed in order to monitor tool wear. This situation may arise when a single axis piezoelectric actuator is simultaneously used as an actuator and a sensor, e.g. its vibration control feedback signal exploited for monitoring purposes. The current paper therefore compares processing of a linear combination of cutting force components to the reference case of processing orthogonal components. Reconstruction of the dynamic force acting at the tool tip from signals obtained during measurements using a strain gauge instrumented tool holder in a turning process is described. An application of this dynamic force signal was simulated on a filter-model of that tool holder that would carry a self-sensing actuator. For comparison of the orthogonal and unidirectional force component tool wear monitoring strategies the same time-delay neural network structure has been applied. Wear-sensitive features are determined by wavelet packet analysis to provide information for tool wear estimation. The probability of a difference less than 5 percentage points between the flank wear estimation errors of above mentioned two processing strategies is at least 95 %. This suggests the viability of simultaneous monitoring and control by using a self-sensing actuator.     

Online tool wear prediction system in the turning process using an adaptive neuro-fuzzy inference system

Tool wear is a detrimental factor that affects the quality and tolerance of machined parts. Having an accurate prediction of tool wear is important for machining industries to maintain the machined surface quality and can consequently reduce inspection costs and increase productivity. Online and real-time tool wear prediction is possible due to developments in sensor technology. Recently, various sensors and methods have been proposed for the development of tool wear monitoring systems. In this study, an online tool wear monitoring system was proposed using a strain gauge-type sensor due to its simplicity and low cost. A model, based on the adaptive network-based fuzzy inference system (ANFIS), and a new statistical signal analysis method, the I-kaz method, were used to predict tool wear during a turning process. In order to develop the ANFIS model, the cutting speed, depth of cut, feed rate and I-kaz coefficient from the signals of each turning process were taken as inputs, and the flank wear value for the cutting edge was an output of the model. It was found that the prediction usually accurate if the correlation of coefficients and the average errors were in the range of 0.989–0.995 and 2.30–5.08% respectively for the developed model. The proposed model is efficient and low-cost which can be used in the machining industry for online prediction of the cutting tool wear progression, but the accuracy of the model depends upon the training and testing data.     

A sensor fusion and support vector machine based approach for recognition of complex machining conditions

During the machining process of thin-walled parts, machine tool wear and work-piece deformation always co-exist, which make the recognition of machining conditions very difficult. Existing machining condition monitoring approaches usually consider only one single condition, i.e., either tool wear or work-piece deformation. In order to close this gap, a machining condition recognition approach based on multi-sensor fusion and support vector machine (SVM) is proposed. A dynamometer sensor and an acceleration sensor are used to collect cutting force signals and vibration signals respectively. Wavelet decomposition is utilized as a signal processing method for the extraction of signal characteristics including means and variances of a certain degree of the decomposed signals. SVM is used as a condition recognition method by using the means and variances of signals as well as cutting parameters as the input vector. Information fusion theory at the feature level is adopted to assist the machining condition recognition. Experiments are designed to demonstrate and validate the feasibility of the proposed approach. A condition recognition accuracy of about 90 % has been achieved during the experiments.     

EEMD方法在刀具磨损状态识别的应用

总体经验模态分解(EEMD)方法在EMD的基础上消除了模态混叠的现象,从而更能准确地揭露出信号特征信息。根据声发射信号的非稳态、非线性的特点,提出一种基于EEMD应用于刀具磨损状态识别的方法。通过EEMD获取无模态混叠的IMF分量;通过敏感度评估算法从所有IMF分量中提取敏感的IMF;提取敏感IMF的能量作为支持向量机(SVM)分类器的输入,将刀具分成正常切削、中期磨损和严重磨损3种状态。通过比较EEMD与应用EMD等方法的分类准确率,确立了基于EEMD的方法在提取刀具磨损状态特征信息的优势。     

基于小波神经网络监测刀具状态的研究

针对切削过程中振动信号和AE信号的特点，提出一种基于小波分析和BP神经网络的刀具磨损监测系统。该系统能融合振动和AE信号的特征，描述信号特征与刀具状态的非线性关系，以此识别刀具状态。试验表明基于小波神经网络的刀具磨损状态监剩系统是有效的。