基于表面二维PCA重构图像的刀具磨损分形特征研究

根据加工表面纹理图像与刀具几何形状之间的内在联系,提出利用计算机视觉技术进行刀具磨损状态监测,设计了基于表面微观纹理图像的刀具磨损状态监测实验系统。提出从二维PCA重构图像中提取分形特征值来判断刀具的磨损状态,给出了二维PCA图像重构算法。理论分析和实验证明:PCA重构图像消除了原始图像信息中的冗余和噪声,从重构图像中提取出来的分形布朗运动维数与刀具磨损有着很强的相关性,可以间接判断刀具磨损情况,从而达到对刀具状态进行监测的目的。     

刀具磨损的机器视觉监测研究

为提高铣削加工时的刀具利用率、降低刀具成本,提出采用机器视觉技术在机监测铣刀磨损状态,及时更换刀具。建立刀具磨损监测系统,由电荷耦合器件(Charge coupled device,CCD)相机获取刀具磨损图像,通过图像预处理、阈值分割、基于Canny算子和亚像素的边缘检测方法建立刀具磨损边界,提取刀具磨损量。开展GH4169镍基高温合金铣削实验,将监测系统检测的磨损量与超景深显微镜的测量结果进行比对,结果表明:该系统具有较高的检测精度,可实现铣削加工时刀具磨损状态的在机监测。     

基于像素空间投影分析的刀具磨损状态监测

采用基于工件表面纹理图像的像素空间投影的统计分析方法来判断刀具的磨损状态，通过对纹理图像的Sobel算子转换以及梯度图像的阈值化来统计像素空间投影后形成的累计面积，并分析该累计面积的变化趋势以判断刀具的磨损情况。实验结果表明，基于像素空间投影的纹理分析方法能够较好的适用于刀具状态监测。     

基于加工表面小波包能量分布比例的刀具磨损状态研究

将计算机视觉检测技术应用于刀具磨损检测。通过获取工件表面图像,利用数字图像处理技术对工件表面图像进行处理。由于工件表面图像信息主要集中在中频区域,利用小波包分解工件表面图像,可得到许多含有丰富中频信息的子图像,故可从中提取小波包能量分布比例特征,作为刀具磨损的度量指标。理论分析和试验结果表明,该方法是有效的。     

基于马尔可夫随机场工件表面纹理模型的刀具状态监测

基于马尔可夫随机场理论，建立了工件表面纹理图像的马尔可夫随机场纹理模型，并对工件表面纹理图像的特点进行了分析。在实验数据的基础上，对工件表面纹理图像的特征参数进行提取，提出采用相对距离作为刀具磨损程度的评价指标。指出三阶马尔可夫随机场能比较充分地反映工件表面纹理图像的特征。实验结果表明，基于马尔可夫随机场的工件表面纹理分析方法能够较好地适用于刀具状态监测。     

基于计算机视觉的刀具磨损检测技术的研究

针对难加工材料切削过程中刀具磨损检测自动化程度低和定量检测困难等问题搭建了一套基于计算机视觉的刀具磨损检测系统.通过分析刀具表面由磨损区向非磨损区过渡边缘的灰度变化特性,设定阈值初步分割磨损带,采用灰度梯度与灰度矩精确定位磨损带边缘,最后重建磨损图像,处理图像数据得到磨损量Vb.通过将该方法计算结果与显微镜测量结果比较后证明该系统具有较高的测量精度.     

基于加工表面盒维数的刀具磨损状态研究

在车削加工过程中,随着刀具磨损量的增加,在工件表面的纹理结构发生变化,依据工件纹理的变化能够间接判断刀具的磨损程度。将分形理论引入到基于图像的刀具状态监测领域,研究二维离散图像信号盒维数的具体实现算法以及盒维数与刀具磨损量之间的变化关系。实验表明:随着刀具磨损量的增加,盒维数具有缓慢上升的趋势,利用这一特征可有效实现刀具磨损状态的监测。     

基于MATLAB的积屑瘤图像处理与分析

在金属切削中,积屑瘤对刀具使用寿命和工件表面质量有很大的影响,应用MATLAB对由机器视觉系统获得的刀具二维图像进行边缘检测,并采用图像相减法来计算积屑瘤的面积,获得了清晰的刀具轮廓图像和积屑瘤大小随时间的变化曲线,实现了刀具积屑瘤变化的在机监测。     

基于机器视觉的刀具几何参数测量技术

在金属零件加工过程中,加工刀具不可避免的产生磨损.如不能及时采取描施,将产生很大的加工误差.把基于计算机图像处理的机器视觉技术引入到刀具参数测量中,提出利用计算机视觉代替人艰对刀具参数进行自动测量的方法,并在Delphi平台上开发了一种专用测量软件系统,利用图像预处理和边缘检测等处理技术,实现了刀具几何参教测量的自动化.应用结果表明,基于机器视觉的刀具几何参数测量系统克服了人工测量所造成的各种误差,重复测量精度达到2μm,大大提高了测量精度和效率.     

基于区域生长法的数控刀具磨损状态检测方法

研究了一种基于视觉特征在线提取刀具磨损特征值的方法,用于不停机诊断刀具磨损状态。针对刀具磨损图像的特点,设计了自动选取种子点与生长阈值的区域生长算法分割磨损区,并通过最小外接矩形提取刀具磨损特征值(VB_(max))。图像处理结果显示,该方法可以有效而便捷地获得刀具后刀面的磨损信息,可用于数控机床不停机检测刀具磨损状态。     

In-cycle monitoring of tool nose wear and surface roughness of turned parts using machine vision

Tool wear has been extensively studied in the past due to its effect on the surface quality of the finished product. Vision-based systems using a CCD camera are increasingly being used for measurement of tool wear due to their numerous advantages compared to indirect methods. Most research into tool wear monitoring using vision systems focusses on off-line measurement of wear. The effect of wear on surface roughness of the workpiece is also studied by measuring the roughness off-line using mechanical stylus methods. In this work, a vision system using a CCD camera and backlight was developed to measure the surface roughness of the turned part without removing it from the machine in-between cutting processes, i.e. in-cycle. An algorithm developed in previous work was used to automatically correct tool misalignment using the images and measure the nose wear area. The surface roughness of turned parts measured using the machine vision system was verified using the mechanical stylus method. The nose wear was measured for different feed rates and its effect on the surface roughness of the turned part was studied. The results showed that surface roughness initially decreased as the machining time of the tool increased due to increasing nose wear and then increased when notch wear occurred.     

[设备健康管理]基于机床信息的加工过程刀具磨损状态在线监测

为实现刀具磨损状态的在线监测,提高监测系统的实用性,提出一种基于机床信息的加工过程刀具磨损状态在线监测方法。采用OPC UA通信技术在线采集与存储数控机床信息,得到与磨损相关的机床内部过程信息,并基于这类信息与相应的刀具磨损信息,利用卷积神经网络建立了刀具磨损状态识别模型。应用案例证明了该方法的监测性能,与其他传统监测方法相比,该方法更适用于实际的生产加工。     

Surface Texture Indicators of Tool Wear - A Machine Vision Approach

There has been much research on the automated monitoring of cutting tool wear. This research has tended to focus on three main areas that attempt to quantify the cutting tool condition: monitoring of specific machine tool parameters in order to infer tool condition, direct observations made on the cutting tool; and measurements taken from the chips produced by the tool. However, considerably less work has been performed on the development of surface texture sensors that provide information on the condition of the tool employed in machining the surface. A preliminary experimental study is presented for accomplishing this texture analysis using a machine vision-based sensor system. In particular, an investigation of the condition of a two-flute end mill used in a standard face milling operation is presented. The degree of tool wear is estimated by extracting three parameters from video camera images of the machined surface. The performance of three image-processing algorithms, in estimating the tool condition, is presented: analysis of the intensity histogram; image frequency domain content; and spatial domain surface texture.     

On-machine tool prediction of flank wear from machined surface images using texture analyses and support vector regression

In this paper, a method for on-machine tool condition monitoring by processing the turned surface images has been proposed. Progressive monitoring of cutting tool condition is inevitable to maintain product quality. Thus, image texture analyses using gray level co-occurrence matrix, Voronoi tessellation and discrete wavelet transform based methods have been applied on turned surface images for extracting eight useful features to describe progressive tool flank wear. Prediction of cutting tool flank wear has also been performed using these eight features as predictors by utilizing linear support vector machine based regression technique with a maximum 4.9% prediction error.     

Notch wear detection in cutting tools using gradient approach and polynomial fitting

Cutting tool wear is well known to affect the surface finish of a turned part. Various machine vision methods have been developed in the past to measure and quantify tool wear. The two most widely measured parameters in tool wear monitoring are flank wear and crater wear. Works carried out by several researchers recently have shown that notch wear has a more severe effect on the surface roughness compared to flank or crater wear. In this work, a novel gradient detection approach has been developed to detect the presence of micro-scale notches in the nose area of the cutting tool. This method is capable of detecting the location of the notch accurately from a single worn cutting tool image.     

基于计算机视觉的刀具磨损状态识别技术

刀具磨损状态自动识别系统能够显著地降低制造成本,但是,只有很少的刀具磨损检测理论能够应用到工业实践中去,特别是基于声发射和切削力测量的间接测量技术。本文综述了作为直接测量技术的计算机视觉系统的优点和它的组成及对刀具图像进行处理的基本原理。     

基于机器视觉的积屑瘤在线监测装置

现代切削工艺中,产生积屑瘤对切削过程和加工表面质量的影响不容忽视,对积屑瘤的在线监测显得尤为重要。设计制作了一种基于机器视觉技术的积屑瘤在线监测装置,能够方便地在机获取切削加工中积屑瘤变化的清晰图像。实验结果表明,该装置简便可靠,为于最终实现刀具磨损在线监测提供了手段。     

介绍一种车床刀具磨损视觉检测装置

对于现代机床而言,刀具的磨损状态监测显得日益重要。在此设计并制作出了一种新颖的在机视觉检测装置,不仅可以实现视觉系统与机床的结合,而且具有良好的隔振性能。实验结果表明,采用这种机构,可以使摄像机拍摄到比较清晰的图像,为实现车刀磨损在机检测提供了条件。     

基于主轴电流信号多特征融合的刀具磨损状态监测

为实现在正常生产条件下进行刀具磨损的长期在线监测,提出了基于主轴电流信号和粒子群优化支持向量机模型(PSO-SVM)的刀具磨损状态间接监测方法。首先对数控机床主轴电机电流信号进行分析,将与刀具磨损相关的主轴电流信号多个特征参数和EMD能量熵进行特征融合作为输入特征向量;其次,通过粒子群寻优算法(PSO)对支持向量机模型(SVM)参数进行优化,建立基于主轴电流信号融合特征和PSO-SVM理论的刀具磨损状态识别模型;最后,通过实验采集某立式加工中心主轴在刀具不同磨损状态下电流信号进行验证,并与传统SVM模型、BP神经网络模型进行了对比分析。结果表明,所提出的方法具有较高的准确率和较好的泛化能力。能够实现正常生产条件下对刀具磨损的长期在线监测。