基于小波包和BP神经网络的刀具磨损状态识别

刀具磨损状态影响金属切削过程,因此监测刀具磨损状态对提高产品质量有着重要的意义。设计刀具磨损状态监测系统,利用传感器采集刀具振动信号,通过小波包对振动信号进行数据分析,并把不同频段的能量值作为刀具磨损状态的特征值,建立BP神经网络,从而在刀具磨损状态和振动信号特征向量之间建立映射关系,完成刀具磨损状态的监测。利用C++Builder和Matlab软件混合编程实现了系统的功能。试验表明,系统运行良好,能够对刀具磨损状态进行正确识别。     

深孔钻削监测系统的研究

通过采用一种新研制的应变式传感器和振动传感器,采集四路信号共同监测深孔加工过程中刀具的磨损状态。在大量实验的基础上,分别对深孔钻削过程中的总轴向力、切向扭矩、垂直方向振动和水平方向振动四种信号进行分析,提取反映刀具磨损的特征量,最后用模式识别的方法进行状态分类,得出监测结果。     

基于人工蜂群-BP神经网络的刀具磨损监测

为了提高刀具磨损状态监测准确度,提出基于人工蜂群-BP神经网络算法的刀具磨损状态监测方法。使用力传感器和振动传感器设计了磨损状态监测平台;使用匹配追踪算法对信号进行了去噪;提取了信号时域、频域、时频域的特征参数,使用核主成分分析法对特征参数进行了降维,确定了反应刀具磨损状态的15个特征参数;提出了人工蜂群-BP神经网络算法的刀具磨损状态识别方法,使用人工蜂群算法优化BP神经网络算法参数;经实验验证,传统BP神经网络识别准确率为78.75%,优化BP神经网络算法识别准确率为100%。     

基于振动信号的铣刀磨损状态识别

刀具磨损状态的变化是自动化加工中最为常见的影响因素,对刀具磨损状态的有效识别能够保证自动化生产的顺利进行.从铣削加工的振动信号中,可以获取刀具磨损状态的信息.基于小波理论,通过分解提取的振动信号,并分析频带内振动信号幅值的变化,就能够确定刀具磨损的状态.对铣削平面和斜面进行了实验分析,证明该种方法能够实现铣削加工过程中刀具磨损状态的有效识别.     

不同信号融合的立铣刀刀具磨破损状态监测方法研究

首先对比研究了不同监测信号的特点,选择了振动信号、切削力信号、声发射信号作为研究信号,然后采用经典信号分析方法对三类监测信号进行分析并提取了相应的统计特征;接着对非平稳性较强的振动信号和声发射信号进行了非平稳特征分析;在此基础上,分别引入相对向量机算法与概率神经网络算法对刀具磨损状态进行识别,基于单一传感器信号的最佳特征向量进行特征层融合,建立了不同信号融合的刀具状态监测模型;最后通过对比不同信号融合方式下的刀具状态监测模型,得到最佳的振动信号与切削力信号融合的监测方式。     

基于小波模糊神经网络刀具监控系统研究

针对切削过程中振动信号和AE信号的特点,利用小波分析技术提取信号深层特征,建立了新型的基于模糊推理的神经网络模型,该模型能融合振动和AE信号的特征和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。试验表明小波模糊神经网络对提高在线刀具监控系统的可靠性极为有效。     

基于LabVIEW的刀具磨损监测分形识别平台

将分形应用在刀具状态监测中,随着刀具磨损量的增加,刀具与工件之间的磨损加剧,振动信号的波形变化越来越不规则,信号的分形维数逐渐增大.盒维数和信息维数变化较小,但变化趋势明显;关联维数的变化相对较大,新刀的关联维数最小,报废刀的关联维数明显增大.识别结果表明,刀具在整个磨损历程中振动信号分形维数的变化规律,其大小能较好地反映刀具不同磨损状态,运用振动信号的分形维数可以有效实现刀具磨损状态的监测.     

基于J-EEMD的刀具磨损状态特征提取技术

在实际刀具状态监测的过程中,通过传感器所直接测得的数据都包含了大量的噪声信号,因此难以从中获取刀具磨损状态的变化规律,这样显然不利于进行模式识别。应用近似联合对角化下的集合经验模态分解(J-EEMD)对观测信号进行处理,基于信号本身特征,自适应地将切削加工中检测得到的振动和声发射信号分解为多个内蕴模式函数(IMF),然后根据各个IMF之间的能量比对变换,提取出了不同磨损状态下的刀具状态特征。实验证明:在该方法对测得数据进行处理的基础上,能够很好地识别出刀具磨损程度的不同状态。     

基于动态树理论的刀具磨损监测技术

提出了基于动态树理论的刀具磨损监测方法,通过相关系数法提取传感器信号与刀具磨损最相关的几组特征,并采用具有局部记忆的B样条模糊神经网络建立刀具磨损量与声发射信号、切削力信号和振动信号特征之间的非线性映射关系,构造了任意加工条件下的刀具磨损监测系统,刀具磨损的识别结果由集成神经网络输出。试验结果表明,基于此方法构建的刀具磨损监测系统具有精度高、可靠度强、增殖性好和在线识别速度快等优点,值得工业推广。     

基于深度SSDAE网络的刀具磨损状态识别

针对刀具磨损状态识别过程中采集数据量大、干扰信号复杂且需人为选择特征参数的问题,为提高刀具磨损状态识别模型的鲁棒性与泛化性,提出了一种数据驱动下深度堆叠稀疏降噪自编码（stacking sparse denoising auto-encoder,简称SSDAE）网络的刀具磨损状态识别方法,实现隐藏在数据中深层次的数据特征自动挖掘。首先,将原始振动信号分解为一系列固有模态分量（intrinsic mode function,简称IMF）,并采用皮尔逊相关系数法选取了最优固有模态来组合一个新的信号;其次,采用SSDAE网络自适应提取特征后对刀具磨损阶段进行了状态识别,识别精度达到98%;最后,对网络模型进行实验验证,并与最常用的刀具磨损状态识别方法进行了对比。实验结果表明,所提出的方法能够很好地处理非平稳振动信号,对不同刀具磨损阶段状态的识别效果良好,并具有较好的泛化性能和可靠性。     

复杂曲面加工过程中铣刀在线监测方法

在验证了铣削力与刀柄摆动电涡流位移信号之间存在线性关系的基础上,以机床主轴端部x,y方向的加速度信号二次频域积分结果作为刀柄摆动位移信号,提取积分位移信号的基频及其谐波信号作为监测信号,解决了电涡流位移传感器安装不便的问题,同时有效去除了干扰信号的影响。利用时域同步平均(time synchronous averaging,简称TSA)计算监测信号的一阶和二阶累积量,结合时域指标方差、偏斜度、峭度、绝对均值及有效值定量刻画累积量波形,通过设定阈值实现状态预警,较好地解决了复杂曲面加工过程中铣刀状态在线监测与预警的难题。     

Real‐Time Monitoring of Tool Fracture in Turning Using Sensor Fusion

The sensor fusion method using both an acoustic emission (AE) sensor and a built-in force sensor is introduced for on-line tool condition monitoring during turning. The cutting force was measured by a built-in piezoelectric force sensor, which was inserted in the tool turret housing of an NC lathe. FEM analysis was carried out to locate the most sensitive position for the sensor. A burst of AE signal was used as a triggering signal to inspect the cutting force. A significant drop in cutting force indicated tool breakage. The algorithm was implemented in a DSP board and the monitoring system was installed on a CNC lathe in an FMS line for in-process tool-breakage detection. The proposed system showed an excellent monitoring capability.     

Development of a system for monitoring tool condition using acousto-optic emission signal in face turning—an experimental approach

In automated manufacturing systems, one of the most important issues is accurate detection of the tool conditions under given cutting conditions so that worn tools can be identified and replaced in time. In metal cutting as a result of the cutting motion, the surface of workpiece will be influenced by cutting parameters, cutting force, and vibrations, etc. But the effects of vibrations have been paid less attention. In the present paper, an investigation is presented of a tool condition monitoring system, which consists of a fast Fourier transform preprocessor for generating features from an online acousto-optic emission (AOE) signals to develop a database for appropriate decisions. A fast Fourier transform (FFT) can decompose AOE signals into different frequency bands in the time domain. Present work uses a laser Doppler vibrometer for online data acquisition and a high-speed FFT analyser used to process the AOE signals. The generation of the AOE signals directly in the cutting zone makes them very sensitive to changes in the cutting process due to vibrations. AOE techniques is a relatively recent entry into the field of tool condition monitoring. This method has also been widely used in the field of metal cutting to detect process changes like displacement due to vibration and tool wear, etc. In this research work the results obtained from the analysis of acousto-optic emission sensor employs to predict flank wear in turning of AISI 1040 steel of 150 BHN hardness using Carbide insert and HSS tools. The correlation between the tool wear and AOE parameters is analyzed using the experimental study conducted in 16 H.P. all geared lathe. The encouraging results of the work pave the way for the development of a real-time, low-cost, and reliable tool condition monitoring system. A high degree of correlation is established between the results of the AOE signal and experimental results in identification of tool wear state.     

CONDITION MONITOR OF DEEP-HOLE DRILLING BASED ON MULTI-SENSOR INFORMATION FUSION

A condition monitoring method of deep-hole drilling based on multi-sensor information fusion is discussed. The signal of vibration and cutting force are collected when the condition of deep-hole drilling on stainless steel OCr17Ni4Cu4Nb is normal or abnormal. Four eigenvectors are extracted on time-domain and frequency-domain analysis of the signals. Then the four eigenvectors are combined and sent to neural networks to dispose. The fusion results indicate that multi-sensor information fusion is superior to single-sensor information, and that cutting force signal can reflect the condition of cutting tool better than vibration signal.     

野外文物安防姿态传感器的信号优化设计

提出对野外文物安防系统中的振动传感器进行混合源信号的优化处理,通过独立量分析变换将混合信号分离成单个信号,再经过小波变换处理后和自定义比对库中的预定值进行匹配,从而确定是否报警。设计提高振动传感器对振动源的识别度,降低振动传感器的误报警率,达到对目标信号源的优化识别。     

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.     

基于PCA和动态监测模型的刀具寿命在线检测技术

为了实现机械加工过程中刀具寿命在线准确识别,采用时域、频域和小波变换等信号分析方法,提取切削力信号和振动信号中与刀具寿命变化敏感的多个特征,系统输入特征向量通过主向量分析(PCA)方法根据累积贡献率进行优化选择;监测系统根据加工条件自动选择对应的,由两个寿命计算模型构成的动态监测模型,两个模型根据输出精度交替实现刀具寿命计算、在线学习和模型参数更新,最终实现了刀具寿命的在线预测。长期运行结果证明,建立的刀具寿命监测系统能够准确预测刀具的寿命状态,具有良好的自学习能力,在线计算速度高,具有较强的工业推广价值。     

Application of backpropagation neural network for spindle vibration-based tool wear monitoring in micro-milling

This study develops a micro-tool condition monitoring system consisting of accelerometers on the spindle, a data acquisition and signal transformation module, and a backpropagation neural network. This study also discusses the effect of the sensor installations, selected features, and the bandwidth size of the features on the classification rate. To collect the vibration signals necessary for training the system model and verifying the system, an experiment was implemented on a micro-milling research platform along with a 700?μm diameter micro-end mill and a SK2 workpiece. A three-axis accelerometer was installed on a sensor plate attached to the spindle housing to collect vibration signals in three directions during cutting. The frequency domain features representing changes in tool wear were selected based on the class mean scatter criteria after transforming signals from the time domain to the frequency domain by fast Fourier transform. Using the appropriate vibration features, this study develops and tests a backpropagation neural network classifier. Results show that proper feature extraction for classification provides a better solution than applying all spectral features into the classifier. Selecting five features for classification provides a better classification rate than the case with four and three features along with the 30?Hz bandwidth size of the spectral feature. Moreover, combining the signals for tool condition from both direction signals provides a better classification rate than determining the tool condition using a one-direction single sensor.