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.     

Low damage lamella preparation of metallic materials by FIB processing with low acceleration voltage and a low incident angle Ar ion milling finish

Metallic materials are known to be very sensitive to Gallium (Ga) focused ion beam (FIB) processing. Crystal defects formed by FIB irradiation degrade the transmission electron microscope image quality, and it is difficult to distinguish original defects from FIB process-induced damage. A solution to this problem is the low acceleration voltage and low incident angle (LVLA) Argon ion milling, which can be incorporated as an extensional countermeasure for FIB damage removal and eventually for preparation of high-quality lamellae. The transmission electron microscope image quality of iron single crystal could be improved by removing crystal defects using the low acceleration voltage and low incident angle Argon ion milling finish. Lamella quality of the processing result was almost similar with that of the conventional electrolytic polishing. As a practical application of the process, low damage lamella of stainless cast steel could be prepared. Effectiveness of the FIB system equipped with the low acceleration voltage and low incident angle Argon ion milling function as a tool to make high-quality metallic material lamellae is illustrated.     

缺陷散射对相控阵超声全聚焦成像的影响研究

相控阵超声全聚焦成像充分利用了检测信号,具有成像精度高、可进行缺陷识别等特点,是未来最具应用前景的相控阵成像算法之一。然而,目前相控阵超声全聚焦成像仍不能实现缺陷的高分辨率成像,无法对缺陷进行准确的定性、定量分析。为此,采用有限元仿真相控阵传感器的全阵列采集(FMC)过程,在全矩阵数据的基础上设计全聚焦成像程序,对圆孔和裂纹两种典型缺陷进行TFM成像,研究典型缺陷的TFM成像规律,从缺陷散射的角度分析影响相控阵超声全聚焦成像的因素。结果表明,实际检测中相控阵超声传感器只能接收到缺陷的部分散射信息,而相控阵超声在缺陷处的散射场分布与缺陷的类型、尺寸、角度及入射波类型、入射角度等因素有关,因此能否接收到缺陷散射的主要能量是影响全聚焦成像精度的关键。     

Prevention of defects in castings using back propagation neural networks

Defects in castings often lead to rejection, which would ultimately result in loss of productivity for a foundry. Expert systems developed by some researchers mostly act as postmortem tools, discussing and analyzing a defect after it has occurred. Though some investigators have attempted to predict a few important defects, a tool that could predict all the possible defects just before the castings are made has not yet been developed. Hence in the present work, an attempt has been made to predict major casting defects like cracks, misruns, scabs, blowholes and air-locks using back-propagation neural networks from the data collected from a steel foundry. The neural network was trained with parameters like green compression strength (GCS), green shear strength (GSS), permeability, moisture percent, composition of the charge and melting conditions as inputs and the presence/absence of defects as outputs. After the training was over, the set of inputs of the casting that is going to be made was fed to the network and the network could predict whether the casting would be sound or defective. If defective, the nature of the defect was also specified by the neural network. The neural network could predict cracks, misruns and air-locks accurately in most of the cases. The neural network could also predict other defects successfully. Investigating the causes followed by altering the moulding parameters and appropriate treatment of the molten metal can prevent the defects that were predicted by the backpropagation neural network.     

基于瞬时频率熵的超声缺陷检测

根据超声无损检测中缺陷和噪声反射回波的瞬时频率的差别，提出利用超声信号瞬时频率在一移动窗内熵的信息，估计缺陷的时频位置。瞬时频率熵方法使得用Wigner—Ville分布表示超声信号时，既能利用Wigner—Ville分布的重要性质，又能实现缺陷的准确定位。对仿真数据和实际探伤数据的实验结果分析表明，瞬时频率熵方法不仅能够识别单个缺陷，也能有效地检测多个缺陷。     

Defects detection of gas pipeline near the welds based on self quotient image and discrete cosine transform

The magnetic flux leakage (MFL) inspection has been widely used in the inline inspection for the evaluation of steel pipelines and plates. In this paper, a defect detection algorithm based on the MFL inspection is proposed for detecting defects near the welds. The defect in this paper means some deformations and deterioration of steel pipes because of corrosions and cracks by humidity and pressure after gas pipes were buried and it doesn’t mean bad welding. The MFL signal of the defects near the welds is worse than that of the far-away from the welds and has low signal-to-noise ratio (SNR). In this paper, the MFL signal of the defects near the welds is enhanced by using the Self Quotient Image (SQI) in this paper and the position of the defects is detected after applying the Discrete Cosine Transform (DCT). Experiments are conducted to demonstrate the accuracy of the proposed defect detection algorithm for the artificial defects carved on the pipes at the pipeline simulation facility (PSF) and the results show that the proposed algorithm can successfully detect the position of the defects on the pipes near the welds.     

压缩机叶轮叶根缺陷相控阵超声检测方法研究

压缩机叶轮叶根易出现疲劳裂纹、气孔等内部缺陷。基于超声脉冲反射法原理,利用相控阵超声检测技术,采用声线模型对缺陷检测位置进行分析,并通过声场分布模拟以及缺陷检测模拟优化实验方案,最后利用聚焦扫描对人工预置平底孔缺陷进行检测。检测结果表明：利用相控阵超声检测技术在叶轮内腔对叶根进行检测,缺陷信号明显,缺陷定位精度高,定位误差仅为0.19%。     

Surface defect detection in tiling Industries using digital image processing methods: Analysis and evaluation

Ceramic and tile industries should indispensably include a grading stage to quantify the quality of products. Actually, human control systems are often used for grading purposes. An automatic grading system is essential to enhance the quality control and marketing of the products. Since there generally exist six different types of defects originating from various stages of tile manufacturing lines with distinct textures and morphologies, many image processing techniques have been proposed for defect detection. In this paper, a survey has been made on the pattern recognition and image processing algorithms which have been used to detect surface defects. Each method appears to be limited for detecting some subgroup of defects. The detection techniques may be divided into three main groups: statistical pattern recognition, feature vector extraction and texture/image classification. The methods such as wavelet transform, filtering, morphology and contourlet transform are more effective for pre-processing tasks. Others including statistical methods, neural networks and model-based algorithms can be applied to extract the surface defects. Although, statistical methods are often appropriate for identification of large defects such as Spots, but techniques such as wavelet processing provide an acceptable response for detection of small defects such as Pinhole. A thorough survey is made in this paper on the existing algorithms in each subgroup. Also, the evaluation parameters are discussed including supervised and unsupervised parameters. Using various performance parameters, different defect detection algorithms are compared and evaluated.     

红外快速缺陷检测的数值模拟

为了定量研究红外无损检测中缺陷深度和形状的问题,首先对带有内部缺陷的平板试件建立了热传导的物理和数学模型,并对不同深度缺陷的情况进行了仿真分析和理论计算,得出了缺陷深度的理论计算方法;然后在已知缺陷深度的情况下利用ANSYS对内部具有不规则缺陷的试件进行了仿真试验,并采用共轭梯度法来定量计算内部缺陷的形状.研究结果表明...     

面向超声导波检测的管道腐蚀建模及仿真实现

超声导波技术被大量应用于管道腐蚀的检测与评估。腐蚀是实际管道中的主要缺陷形式,管道腐蚀缺陷形貌多样且复杂,针对管道腐蚀导波检测的很多研究是通过仿真手段开展的。常用的缺陷简化模型不能充分反映实际腐蚀缺陷的复杂程度,有可能造成分析结果的偏差。本文在分析腐蚀特点基础上提出了基于W-M分形函数的腐蚀仿真模型,研究了管道腐蚀缺陷的有限元自动建模仿真方法,并通过分析讨论超声导波检测不同腐蚀缺陷的仿真结果对模型的有效性进行了验证,结果证明基于本文提出的腐蚀模型所得缺陷回波可提供更丰富的缺陷信息,有利于揭示管道腐蚀特征与导波信号之间的量化关系。本文的研究成果可为进一步分析管道腐蚀缺陷的检测评估建立理论基础。     

基于小波多尺度分解的竹条缺陷检测算法

在竹条缺陷的图像检测过程中,针对缺陷种类多,尺寸和形状变化大且竹纹理干扰大,提出一种竹条缺陷检测算法。首先设计了竹条边界检测算法,以判断竹条的三角条缺陷;其次对竹条图像的小波分解子图像进行32灰度级量化处理,经过分析图像共生矩阵的逆差矩差分值来获取最佳的小波分解层,实现了对竹纹理的滤除;最后结合Otsu自动阈值检测方法,完成了竹条常见的6种缺陷的检测。经过实验,该算法准确稳定,能有效滤除竹纹理的干扰,竹条缺陷检测效果较好。     

基于智能图像识别的烟盒包装质量快速检测系统的研究

提出一种基于智能图像识别技术的烟盒包装瑕疵检测方法,将图像处理为灰度值矩阵后,通过感兴趣区域和 Sobel 算子边框查找法进行瑕疵快速识别。提出一种基于机器学习和 SVM s 模式识别的分级算法,实现包装瑕疵的快速分类。建立了盒装香烟包装质量检测系统模型,验证所提算法具有正确识别率高和误判率合理等优点,对构建基于智能图像识别的产品检测系统具有一定的工业参考价值。     

An effective defect identification scheme in pipeline ultrasonic testing

The identification of a defect from its pulse-echo ultrasonic signal is a difficult task but greatly significant for defect evaluation in pipeline ultrasonic testing. In this work, an effective defect identification scheme, which uses the deconvolution technique, is developed to identify the ultrasonic signals from simulated defects with two different shapes (a notch and a transverse cylindrical cavity) on a pipeline specimen. A DSF (Defect Similarity Function) defined as a deconvolution of a target signal by a reference signal is used to classify the defects. Experimental results indicate that the DSFs for the same type of defect-reference signals are narrow impulselike patterns with larger amplitudes, while those for the different types of defect-reference signals are broad impulselike patterns with relatively smaller amplitudes. Furthermore, the DSF is sensitive to defect shapes but non-sensitive to the measuring distance and defect size. Therefore, the defects can be identified from the pattern of the DSF. The proposed technique shows great potential for the identification of defects via ultrasonic signals in the pipeline inspection. This text was submitted by the authors in English.     

基于小波和灰度共生矩阵的带钢表面缺陷识别

提出了一种基于小波变换和灰度共生矩阵的带钢表面缺陷识别方法。采用小波变换分解缺陷图像并提取其低频子带信息。通过在低频子带上构造0°、45°、90°和135°四个方向的灰度共生矩阵,分别计算角二阶矩、熵、对比度和逆差矩4个特征值,共获得16个特征值,并将其输入支持向量机,完成对6类共1800张带钢表面缺陷图像的识别,总体识别精度大于96%。实验结果表明,小波变换与灰度共生矩阵结合能有效描述带钢表面缺陷纹理特征,具有较好的识别效果。     

Automatic optical inspection for detecting defects on printed circuit board inner layers

This paper studies automatic optical inspection for detecting defects on the printed circuit board inner layer. The development of this study can be divided into five stages, they are reference image rebuilding, inspection image normalization, image subtraction, defects separation and defect classification. In the image subtraction stage, the difference between the reference image from the printed circuit board design and the inspected image is checked for defects. Each defect region is separated using a defect outer boundary tracing method. A boundary state transition method is proposed to classify the defect types. This system can recognize eight defect types, open, mouse bite, pinhole, missing conductor, short, spur, excess copper and missing hole. In addition, a comparison with the methods described in the literature is made, proving that the proposed method produces better results .     

交变磁场下焊接缺陷磁光成像特征分析

研究焊接缺陷磁光成像检测方法,基于法拉第旋转效应,分析交变磁场下焊接缺陷磁光成像特征与漏磁场之间的关系.建立焊接缺陷的三维有限元模型,对不同类型和宽度的焊接缺陷漏磁场分布进行模拟,并在交变磁场激励下对不同焊接缺陷进行磁光成像无损检测试验,通过试验验证了焊接缺陷检测模型的有效性.研究结果表明,漏磁场分布与缺陷的类型和宽度...     

软性电路板金面缺陷的无监督检测

为实现软性电路板(FPC)金面缺陷的准确自动检测,提出了一种以Gabor滤波器和Mean Shift聚类算法为基础的完全无监督FPC金面缺陷检测方法。首先,用Gabor滤波器组、数学形态学与Gaussian平滑处理抽取待检测图像的多维特征;然后,使用主元分析(PCA)将每个像素特征维数降为二维;最后,使用Mean Shift方法对二维特征数据进行聚类并将聚类的结果转化为二值图像。整个检测过程无需预先知道缺陷的类型和FPC金面的纹理类型,是一种完全无监督的检测方法。对带有各种缺陷的FPC金面进行检测实验,结果表明,该方法能够准确地将各类缺陷区域从背景区域中分离出来,具有自动缺陷检测系统所要求的识别能力强、稳定性高的特点。     

Nondestructive proximate testing of plastic deformations by differential thermal EMF measurements

A method for nondestructive proximate testing of plastic deformations in metals and alloys by measuring the differential thermal emf is considered. Using an instrument that was developed for this problem, thermal emf measurements were performed on several specimens of steels of different types on which notches were made as zones of expected localization of plastic deformations. During extension of specimens, the thermal emf was measured in the plastic-deformation zone. A linear decrease in the thermal emf was observed; the maximum change occurred just before specimen fracture.     

基于磁偶极子能级分布的缺陷反演成像

在建立离散磁偶极子模型的基础上，通过广义逆矩阵方法求解测量方程的最小二乘极小范数解，取得磁偶极子的影响量分布；通过分析及仿真试验，找出缺陷磁偶极子能级分布的规律，从而对缺陷偶极子进行判定、选择，进而进行成像显示，实现缺陷的断层成像，为缺陷的三维可视化奠定了基础。     

Lateral-torsional buckling analysis of thin-walled beams including shear and pre-buckling deformation effects

In this paper, lateral-torsional buckling behavior of open-section thin-walled beams is investigated based on a geometrically nonlinear formulation, which considers the effects of shear deformations. A finite element numerical solution along with an incremental-iterative solution procedure is adopted to trace the pre-buckling as well as the post-buckling equilibrium paths. Formulation is applicable to a general type of open-section and load position effects are also included. Numerical results are validated through comparisons with experimental results and those based on other formulations presented in the literature. Comparisons have also been made between the results based on fully nonlinear analysis and linearized buckling analysis in order to illustrate the effects of pre-buckling deformations as well as the shear deformations on the buckling load predictions. Examples illustrate the influence of beam slenderness and moment gradient on the effects of pre-buckling deformations in predicting bucking loads.