基于机器视觉的机械零件测量技术

应用IMAQ Vision软件构建的机械零件测量系统将机器视觉技术和虚拟仪器技术结合在一起，对各种机械零件进行快速准确的在线测量。这里以齿轮为例介绍了测量的技术路线。测量系统综合应用LabVIEW和它的企业链接、信号处理等工具包，将被测对象的图像和数据直接融合到企业的信息流之中。     

基于机器视觉的零件表面粗糙度测量

机器视觉测量是近年来测量领域中的新测量技术,是以光学为基础,融电子技术、计算机技术、图像处理技术为一体的测量系统。介绍一种基于机器视觉进行表面粗糙度测量的方法。     

机器视觉在工业领域中的研究应用

综述机器视觉在国内外工业领域的研究应用状况,总结机器视觉在应用中存在的问题.     

Ball stud inspection system using machine vision

In this paper, a vision-based inspection system that measures the dimensions of a ball stud is designed and implemented. The system acquires silhouetted images by backlighting and extracts the outlines of the nearly dichotomized images in subpixel accuracy. The sets of boundary data are modeled with reasonable geometric primitives and the parameters of the models are estimated in a manner that minimizes error. Jig-fixtures and servo systems for the inspection are also contrived. The system rotates an inspected object to recognize the objects in space not on a plane. The system moves the object vertically so that it may take several pictures of different parts of the object, resulting in improvement of measuring resolution. The performance of the system is evaluated by measurement of the dimensions of a standard ball, a standard cylinder, and a ball stud.     

基于机器视觉系统的磁芯零件在线分级

机器视觉已成为无损检测技术中的一个重要分支。利用机器视觉系统获取磁芯产品图像,通过图像预处理、图像分割、特征提取、图像分析、输出检测结果的控制信号,再借助于PLC控制执行机构,最终达到对磁芯产品进行在线分级的目的。     

机器视觉技术在螺纹检测中的应用

这里提出了一种基于机器视觉的螺纹非接触检测技术,利用CCD作为螺纹几何要素的探测器件,借用模式识别、图像处理技术,实现了对螺纹中径、螺距、牙型角三个重要参数的在线测量。实验说明,应用机器视觉检测螺纹检测速度快,检测精度高,是螺纹检测的发展方向。     

Restoration of blurred images for surface roughness evaluation using machine vision

An attempt is made to evaluate the surface roughness of uniformly moving machined surface (grinding, milling) using machine vision technique. In the case of moving surfaces the images are likely to blur due to the relative motion between the CCD camera and the object to be captured. Hence the degraded image has to be restored by removing distortion due to motion before subsequent analysis. In this work, image blur due to motion is considered, in particular, blur that occurs when the motion is uniform at constant speed and in a fixed direction. The blurred image is modeled as a convolution between the original image and a known point spread function. The Richardson–Lucy Restoration algorithm, a method of estimation based on Bayes theorem has been used to correct the image. The algorithm is tested in simulations and in practical experiments. A simulation gives complete control over the setup and enables to test the performance of the algorithm. The quantification of roughness for restored images are performed using the statistical parameters such as spatial frequency, arithmetic average of gray level and standard deviation after pre-processing. An Artificial Neural Network (ANN) was used with these three statistical parameters as input to predict the vision roughness. Finally, vision roughness values calculated using the deblurred images are compared with the stylus roughness value. An analysis based on the comparison to understand the validity of the present approach of estimation of surface roughness based on the digitally processed images for implementation in practice, is presented in this paper.     

基于机器视觉系统的颜色识别

应用NI公司应用程序开发平台labVIEW,通过编写程序建立虚拟仪器系统并组建简单的外部硬件设备系统,实现了对图像的采集和处理,可以识别出不同颜色的物体,以及计算出该物体的面积,中心坐标等物理参数。我们可以利用这些参数作为下位机的控制信号,为后续物体的筛选和分级做好准备。经实验证明,采用该机器视觉技术是能准确实现番茄色选的,并得到番茄的物理参数,为后续系统开发研究提供了重要依据,也为机器视觉技术的广泛工程应用提供了重要理论支持。     

基于机器视觉技术的精确定位方法研究及应用

介绍基于机器视觉技术的某反射式多光束引导定位系统的工作原理和误差来源,并针对其误差提出了从硬件和软件两方面减小误差的方法: (1)采用高精度测量仪器校准关键零件位置精度,及采用有限元方法仿真技术获得关键零件的最小形变; (2)采用改进的边缘检测算法,提取精确边,减小因CCD器件本身物理性质带来的误差。通过以上措施,保证了系统精度。     

Bearing defect inspection based on machine vision

Appearance defects inspection plays a vital role in bearing quality control. Human inspection is a traditional way to remove defective bearings, which is instable and time consuming. In this paper, we develop a machine vision system for bearing defect inspection, which can inspect various types of defects on bearing covers, such as deformations, rusts, scratches and so on. The proposed system designs a novel image acquisition system to enhance the defects appearances and get controlled image acquisition environment. A series of image processing methods are proposed or utilized to inspect the defects. Especially, for the deformation defects on seal, we find a common rule on the distribution of projection, and design a simple but effective inspection algorithm based on the rule. The proposed system is evaluated and compared with skilled human by the recall, precision and F-measure. Experimental results show that the proposed vision system has high accuracy and efficiency.     

Measuring wear of the grinding wheel using machine vision

This paper proposes a new method for measuring grinding wheel contours using machine vision. The vision-aided measuring system comprises a CCD coupled with a telecentric lens, back lighting board and frame grabber. Measuring the image of the specimen with a grinded gap substitute directly captures the image of the actual grinding wheel. Using this method makes the 3 D of the topography of the grinding wheel into the 2 D of the contour of the grinding wheel. This method significantly simplifies grinding wheel wear measuring procedures compared with the traditional methods. Therefore, the presented paper provides a new method to improve efficiency and cost on measuring wear of the grinding wheel. The results show that this developed system achieves a repeatable accuracy of ±3 μm for the measurement of the grinding wheel contours.This paper has not been published elsewhere nor has it been submitted for publication elsewhere.     

Effect of lighting conditions in the study of surface roughness by machine vision - an experimental design approach

In the evaluation of surface roughness by machine vision technique, the scattered light pattern reflected from the machined surface is generally captured and optical surface finish parameters from the images are correlated with the actual roughness. Capturing of the image at appropriate conditions is required for the good correlation of the optical parameters with the roughness. Lighting conditions is a major factor that influences the image pattern and hence the optical parameters. In this work the lighting conditions like grazing angle, the light to specimen distance, the orientation of the striations on the surface to the light are varied and its influence on the optical surface finish parameter are studied. A plan of experiments based on the techniques of Taguchi was designed and executed for conducting the trials and to obtain valid conclusions. The analysis of variance and the signal to noise ratio of robust design are employed to investigate the influence of different lighting conditions on the optical surface finish parameter. The results of both the approaches confirm that grazing angle is the most influencing factor affecting the image parameter.     

Design and development of a new machine vision wire bonding inspection system

In this paper, we developed a fast and robust machine vision system for wire bonding inspection. The defects caused by a bonding wire being broken, lost, shifted, shorted, or sagged in an integrated circuit (IC) chip can be inspected automatically. A new lighting environment was devised which will highlight the slope of the bonding wire and suppress the background from being extracted. First, a set of machine vision algorithms were designed to filter out the defects of broken wire, lost wire, shifted wire, and shorted-out wire. Second, the 3-D type defect of sagged wire can be detected by inspecting the change of the bonding wire slope, derived from a single 2-D image. Some illustrations are given to show the efficiency and effectiveness of the proposed novel wire bonding vision inspection system.     

机器视觉在工业测量中的应用与研究

基于普通双目视觉测量模型,针对视觉测量系统的关键技术-标定、特征提取、特征匹配,提出了一种实用的高精度摄像机标定方法,基于“多对一”的思想,讨论了一种改进的Hough变换方法;山外极约束理论,提出了一种基于角点的外极斜率特征匹配方法。实验证明.该方法能成功的应川于复杂零件的测量。     

OpenCV在条烟视觉检测系统中的应用

条烟视觉检测系统是安装在条包生产线上,在条包装箱之前,对其外观缺陷进行检测的一种专用设备。系统可以对生产线上的条包进行实时检测,并及时剔除外观有缺陷的产品,具有高速、高效、准确的优点。该检测系统是集机械、光电、计算机、通信等技术为一体自动化检测系统,系统具有高速、高效、准确的优点,它的使用不仅可以提高烟草生产企业的自动化程度,还可以降低工人的劳动程度,改善工作环境,保证产品的质量。对常见条烟检测项进行了算法设计,并利用某公司的开源计算机视觉库OpenCV进行算法实现,提高了检测速度和精度。     

宽频带RMS转换曲线族自动校准方法的研究

在交流测量中，信号频率的变化是影响交流信号测量准确度的主要原因之一。以对数一反对数运算式交流有效值转换器为研究对象，提出了宽频带交流电压测量的增益和非线性校准的新方法，校准结果表明该方法是有效的，并在50～30kHz频率范围内获得0．06％的准确度。     

机器视觉非接触测量系统在线校准及不确定度分析

高精度、大面阵CCD和CMOS传感器件的出现,使得机器视觉非接触测量技术得到越来越广泛的应用。本研究以典型机器视觉非接触测量模型为研究对象,通过对其原理、测量数学模型和计量特性进行了阐述和分析,创新性提出了一种机器视觉非接触测量系统的在线校准方法,分析并确立了测量过程中的不确定度来源,并对校准结果进行了不确定度评定。实验表明,机器视觉非接触测量系统在线校准方法具备两个优点：一是易操作性,优化了测量标准的追溯关系;二是校准精度高,在95％的置信概率下。校准系统不确定度为0．008mm。     

基于机器视觉的3自由度并联平台标定试验研究

为了提高并联平台的运动精度,以锡膏印刷机3自由度并联平台为研究对象,基于机器视觉提出了一种运动学标定修正系统输入的补偿方法。给出了平台理论分析模型,利用运动学逆解方程推导出误差函数,通过机器视觉对平台标识点进行测量并记录平台运动的实际位姿,根据标定结果对理论控制模型进行补偿。试验结果表明,该标定补偿方法有效地提高了并联平台的运动精度。     

A laser-based machine vision measurement system for laser forming

Laser forming continues to be a promising technology in manufacturing due to its fast speed, flexibility, and low-cost. Measurement of deformation after laser forming is widely needed to verify its convergence to the intended shape in academic research. With the development of laser forming, high requirements on the measurement of the deformed work-piece have been sought such as a 3D profile of the deformed surface, a large measuring range, and measuring convenience. In this paper, a laser-based machine vision measurement system was developed to measure the 3D profile of deformed surface by a one-off scanning process. Based on the 3D profile data, the vertical displacement of the deformed plate was calculated for bending analysis. In addition, as one of the important feature parameters, transverse shrinkage was automatically determined through a novel image-based method during the scanning process. A measuring accuracy of 0.03 mm for vertical displacement measurement and 0.0125 mm for transverse shrinkage were achieved in the developed measurement system. This measurement performance is acceptable in most of the laser forming processes currently studied.     

基于视觉的运动物体特征点参数测量方法研究

主要研究利用机器视觉的非接触式测量方法实现对运动物体特征点的轨迹、速度和加速度三个运动参数的测定。通过在运动物体上设定一个特征点,采用基于运动估计的模板匹配技术实时跟踪特征点在各个时间点的位置。通过对动态图像序列的分析和处理从而在线得到运动物体特征点的三个运动参数。为了实现实时在线测量,采用了基于运动估计的图像匹配算法加速了图像匹配算法的计算速度,实现实时在线测量。此算法计算一次位置点需要的时间为20ms,图像标定的精度为1μs,达到了实时在线测量的要求。测试结果表明:研究的基于运动估计的运动物体特征点测量算法运算速度快、可靠以及测试简单等特点。