一种新型谐振式非接触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刀具磨破损监测研究需要解决的关键技术 .本文根据刀具磨、破损监测的特点 ,研制了既可用于刀具磨损状态监测 ,也可用于刀具破损监测的谐振式高灵敏度流体声发射传感器 .对研制的流体声发射传感器性能进行了实验研究 ,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.     

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.     

单模光纤耦合声发射传感器及其应用研究

根据声发射引起的扰动振动现象.提出采用单模光纤耦合器检测振动的声发射探测技术.单模光纤耦合传感器耦合输出与传感器耦合区长度和振动频率有确定的函数关系.分析和设计了熔锥耦合型单模光纤声发射振动传感器.搭建了相应的等强度悬臂梁振动监测及解调系统,通过与当前使用的压电振动传感器的室内实验,对比测试冲击信号和周期信号,验证了该传感器能有效实现振动扰动的检测.结合岩石试件破裂实验,进一步验证光纤耦合声发射振动传感器是能实现岩石声发射检测的一种新的检测技术方法.光纤耦合振动传感器以其灵敏度高,制作简单,抗干扰能力强,性价比高,使用简单等优点在其他光纤传感器和传统的电类传感器中,具有不可比拟的应用前景.     

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。     

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。     

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。     

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.     

Tool condition monitoring in interrupted cutting with acceleration sensors

Tool breakage is a serious issue in conditions with highly variable stress such as interrupted turning. The tool may fail suddenly though commonly tool failure is preceded by other symptoms such as chipping or fracture of tool edges and tool wear before the complete failure. These symptoms can be used to predict reliably complete tool failure. In the case of a complete failure, the surface integrity of the workpiece is commonly ruined causing waste, making the individual events one of the most expensive failures in small series and flexible manufacturing in addition to collisions. In earlier studies, tool wear has been monitored by force sensors. There are also methods for estimating cutting force with acceleration sensors. In this study, it is demonstrated that it is possible to estimate tool deflection, connected to main cutting force, with acceleration sensor and use this information for detecting the chipping and small fracture of the tool edge. The method presented in this study can be used as a predictor for complete tool failure and thus prevent waste.     

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.     

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.     

P(VDF-TrFE) copolymer acoustic emission sensors

Acoustic emission (AE) sensors have been fabricated using polarized polyvinylidene fluoride/trifluoroethylene (P(VDF-TrFE)) piezoelectric copolymer films. The acoustic and electromechanical properties of the copolymers have been determined using the ultrasonic immersion technique and the resonance technique, respectively. The P(VDF-TrFE) AE sensors have been calibrated according to the ASTM standard and evaluated for potential application in the detection of AE in glass fiber reinforced polypropylene (GFPP). A ceramic AE sensor also has been fabricated using lead zirconate titanate (PZT) 7A piezoelectric ceramic and its sensitivity and performance are reported as well. The copolymer sensors do not show resonance peaks of the ceramic sensor and have adequate sensitivity. They can reproduce AE signals accurately without giving artifacts and have potential use in commercial AE systems.     

Dynamic speed control of a machine tool with stochastic tool life: analysis and simulation

We discuss a tool management model for a flexible machine equipped with a tool magazine, variable cutting speed, and sensors to monitor tool wear, when tool life due to flank wear is stochastic. The objective is to adjust the cutting speed as a function of remaining distance, each time a tool change occurs, in order to minimize the expected processing time (sum of cutting and tool setup time). We address the computational aspects of finding optimal decision rules and we present numerical results suggesting that easily computed decision rules of a simple static model are near-optimal for our dynamic programming model. Dynamic adjustment is assessed with simulation experiments.     

Agile monitoring system for turning of difflicult-to-cut materials

This paper presents an agile monitoring system based on multiple sensors and fuzzy pattern recognition for the detection of tool cutting conditions in turning operations. Three piezoelectric force sensors and a thermocouple (NiCr-Nial) sensor were used to measure cutting force and temperature without altering the machine tool dynamics. Fuzzy C-means algorithm is used to improve the agility of selecting, clustering and classifying cutting tool conditions.     

TSK fuzzy modeling for tool wear condition in turning processes: An experimental study

This paper presents an experimental study for turning process in machining by using Takagi-Sugeno-Kang (TSK) fuzzy modeling to accomplish the integration of multi-sensor information and tool wear information. It generates fuzzy rules directly from the input-output data acquired from sensors, and provides high accuracy and high reliability of the tool wear prediction over a wide range of cutting conditions. The experimental results show its effectiveness and satisfactory comparisons relative to other artificial intelligence methods.