基于小波神经网络的刀具状态监测

建立了一种小波基函数神经网络的切削刀具磨损状态监测系统。通过提取反映刀具磨损状态的特征参数：声发射，主功率，进给电流为输入信号，利用Morlet解析小波神经网络的非线性模型，获得表示刀具磨损状态的特征量，来实现刀具磨损状态在线智能监测。它可以有效地提高系统识别的精确度和可靠性。     

一种新的声发射刀具磨损小波分析方法

提出了一种新的声发射刀具磨损小波判析方法，该方法通过多层小波分解对信号主能量所处频段进行局部特性刻画，利用小波分解系数特征统计值在统计量与刀具状态间建立物理联系。实例表明，该方法能有效地判断刀具状态，比常用的利用神经网络进行状态分析的方法更具有理论直观性与操作的时效性。     

基于细胞神经网络刀具磨损图像的预处理

提出了一种基于细胞神经网络的刀具磨损图像处理方法，通过设计细胞神经网络参数，运用细胞神经网络对刀具的二值图像平滑滤波，边缘提取，通过仿真证明该方法是有效的，由于细胞神经网络易于用VLSI实现并且并行处理速度快，因此该方法对刀具的磨损状态机器视觉检测中的图像处理具有实用意义。     

基于BP神经网络和D-S证据理论的刀具磨损监测方法

将BP神经网络和D-S证据理论相结合的方法运用于刀具磨损监测中,采用小波包分解法对刀具磨损过程中产生的声发射信号进行特征提取,构建特征向量,利用BP神经网络识别判断刀具磨损状态;通过BP神经网络的输出结果和训练误差计算D-S证据理论的基本概率赋值,并用D-S证据理论对BP神经网络的识别结果进行决策级融合。实验结果表明:该方法避免了神经网络识别时的误诊,提高了整个刀具磨损监测系统识别的准确性和可靠性。     

基于粗糙集和BP神经网络的刀具状态监测

在刀具磨损状态监测中,能够提取到的反映不同刀具磨损状态的特征量较大,基于神经网络的状态识别无法去掉冗余特征,会存在训练时间长和准确率降低等问题。针对这些问题,提出基于粗糙集-BP神经网络的刀具磨损状态监测方法,利用粗糙集对特征进行属性约简,去掉冗余信息,从而优化特征,并且减少神经网络的输入端数据,可以缩短神经网络的训练时间和提高识别的准确率。通过对实测刀具数据进行分析,证明了该方法的有效性。     

基于声发射和神经网络的数控机床刀具故障诊断

分析了数控刀具的切削状态,介绍了声发射检测系统和神经网络技术,对刀具切削状态信息声发射检测的可行性和神经网络技术智能诊断方法进行了分析,并通过数控机床刀具故障诊断实例,验证了通过声发射提取刀具切削状态方法的有效性和通过神经网络智能诊断技术检测刀具切削状态方法的正确性。     

基于小波-神经网络纹理图像识别的刀具状态监测

介绍了一种采集旋转工件图像的光学监测系统,提出通过二维小波分析工件纹理图像,提取纹理特征,设计了基于动态和静态神经网络的刀具状态识别系统,该系统可用于自动化加工中刀具诊断,仿真证明了有效性。     

基于切削力的小波神经网络刀具磨损状态监测

为了有效地进行刀具状态监测,采用小波神经网络对刀具进行故障诊断.通过小波变换提取刀具磨损切削力信号的特征,利用小波包分解技术对信号进行分析,得到有效的特征量作为BP神经网络的输入样本,并对网络进行学习训练,完成对刀具磨损状态的有效识别.仿真结果表明该方法是有效的.     

多监控数据融合下的铣削刀具磨损量预测北大核心

提出多监控数据下的刀具磨损精确估计神经网络模型,将多传感器信号数据处理成三种时间特征数据,利用三个相同的子模型分别对特征数据进行处理,并作为多层全连通网络的输入,进而实现刀具磨损的最终估计。使用的子模型结合了Transformer模型和自注意力机制,利用长短期记忆网络(LSTM)增强了刀具磨损的数据获取能力,提高了模型的性能。通过实际铣削数据集的多个实验,验证了该方法的有效性,与其他方法进行了比较,验证了该方法的优越性。     

基于参数策略的改进粒子群优化PNN神经网络刀具磨损研究

刀具磨损直接影响工件加工质量和尺寸精度,正确掌握刀具磨损状态及时换刀,减少机床停机时间,将直接提高加工效率。为提高刀具磨损状态识别准确率,提出一种基于参数策略的改进粒子群优化PNN(IPSO-PNN)神经网络识别刀具的磨损状态。相较于BP神经网络收敛速度慢、易陷入局部最优的缺点,IPSO-PNN神经网络结构简单、训练简洁快速。与BP神经网络和标准PNN神经网络仿真结果对比,结果表明:IPSO-PNN神经网络识别准确率高,收敛速度快,仿真耗时短,能有效提高刀具磨损识别准确率。     

Evaluation of wear of turning carbide inserts using neural networks

Recent trends, being towards mostly unmanned automated machining systems and consistent system operations, need reliable on-line monitoring processes. A proper on-line cutting tool condition monitoring system is essential for deciding when to change the tool. Many methods have been attempted in this connection.Recently, artificial neural networks have been tried for this purpose because of its inherent simplicity and reasonably quick data-processing capability. The present work uses the back propagation algorithm for training the neural network of 5-3-1 structure. The technique shows close matching of estimation of average flank wear and directly measured wear value. Thus the system developed demonstrates the possibility of successful tool wear monitoring on-line.     

Drilling wear detection and classification using vibration signals and artificial neural network

In automated flexible manufacturing systems the detection of tool wear during the cutting process is one of the most important considerations. This study presents a comparison between several architectures of the multi-layer feed-forward neural network with a back propagation training algorithm for tool condition monitoring (TCM) of twist drill wear. The algorithm utilizes vibration signature analysis as the main and only source of information from the machining process. The objective of the proposed study is to produce a TCM system that will lead to a more efficient and economical drilling tool usage. Five different drill wear conditions were artificially introduced to the neural network for prediction and classification. The experimental procedure for acquiring vibration data and extracting features in both the time and frequency domains to train and test the neural network models is detailed. It was found that the frequency domain features, such as the averaged harmonic wavelet coefficients and the maximum entropy spectrum peaks, are more efficient in training the neural network than the time domain statistical moments. The results demonstrate the effectiveness and robustness of using the vibration signals in a supervised neural network for drill wear detection and classification.     

基于小波神经网络的高速铣削刀具磨损识别

刀具磨损一直是制造技术中引人注目的重要问题,对于高速切削来说由于加工成本较高而且刀具价格比较昂贵,因此对高速切削中的刀具状态进行识别和监控具有非常重要的意义.文章通过建立小波神经网络来实现对高速加工中刀具状态的识别,结果与实际情况基本一致,从而表明通过此方法是可以较好的对高速加工刀具状态进行识别的.     

Data fusion neural network for tool condition monitoring in CNC milling machining

Several data fusion methods are addressed in this research to integrate the detected data for the neural network applications of on-line monitoring of the tool condition in CNC milling machining. One dynamometer and one accelerometer were used in the experiments. The collected signals were pre-processed to extract the feature elements for the purpose of effectively monitoring the tool wear condition. Different data fusion methods were adopted to integrate the obtained feature elements before they were applied into the learning procedure of the neural networks. The training-efficiency and test-performance of the data fusion methods were then analyzed. The convergence speed and the test error were recorded and used to represent the training efficiency and test performance of the different data fusion methods. From an analysis of the results of the calculations based on the experimental data, it was found that the performance of the monitoring system could be significantly improved with suitable selection of the data fusion method.     

Drill wear monitoring using neural networks

The primary objective of this research is to monitor drill wear on-line. In this paper, drill wear monitoring is carried out by measuring the thrust force and torque signals. In order to identify the tool wear conditions based on the signal measured, a neural network, using a cumulative back-propagation algorithm, is adopted. This paper also describes the experimental procedure used and presents the results obtained for establishing the neural network. The inputs to the neural network are the mean values of thrust force and torque, spindle rotational speed, feedrate and drill diameter. The neural network is trained to estimate the average drill wear. It is confirmed experimentally that the tool wear can be accurately estimated by the trained neural network. The accuracy of tool wear estimation using the neural network is superior to that using other regression models.     

基于RBF神经网络的数控机床故障诊断研究

本文介绍神经网络用于数控机床控制系统的故障诊断技术，分析了数控机床故障诊断的方法，并采用RBF神经网络实现数控机床控制系统故障诊断的算法和程序设计。     

Using neural network for tool condition monitoring based on wavelet decomposition

This paper presents a neural network application for on-line tool condition monitoring in a turning operation. A wavelet technique was used to decompose dynamic cutting force signal into different frequency bands in time domain. Two features were extracted from the decomposed signal for each frequency band. The two extracted features were mean values and variances of the local maxima of the absolute value of the composed signal. In addition, coherence coefficient in low frequency band was also selected as a signal feature. After scaling, these features were fed to a back-propagation neural network for the diagnostic purposes. The effect on tool condition monitoring due to the presence of chip breaking was studied. The different numbers of training samples were used to train the neural network and the results were discussed. The experimental results show that the features extracted by wavelet technique had a low sensitivity to changes of the cutting conditions and the neural network has high diagnosis success rate in a wide range of cutting conditions.     

基于HPSO优化BP神经网络的刀具磨损状态识别

针对BP神经网络容易陷入局部极值导致识别精度低的问题,文章提出了一种基于混合粒子群算法(HPSO)的BP神经网络优化算法。在刀具磨损监测实验过程中,采集刀具切削的声发射(AE)信号,利用小波包分解算法对AE信号进行滤波,并进行特征提取。将频带能量特征和切削参数分别作为主特征和辅助特征,并对其对归一化处理。采用混合粒子群优化算法(HPSO)对BP神经网络预测模型进行优化,利用优化后的模型对测试样本进行模式识别,结果表明,优化后的HPSO-BP模型能够有效地降低神经网络陷入局部极值的情况,提高刀具磨损识别精度。