无损检测可靠性的模糊定义及其检测概率分析

基于模糊集合理论,建立了在无损检测(NDT)可靠性分析中关于检测概率(POD)的模糊定义.引入半梯形和降岭形的隶属函数,并给出隶属度函数来描述缺陷尺寸与检测概率之间的模糊关系.在此基础上,采用NDT可靠性的对数正态格式,对检测概率进行了计算.     

Optimal signal-adaptive maximum likelihood filter for enhancement of defects in eddy current C-scan images

True estimation of the boundary and size of defects are major problems in eddy current (EC) non-destructive evaluation of conductive materials. EC image processing techniques can be used for better estimation of defect details. Because of non-stationary nature of EC C-scan images and same intensities of noise and defect histograms, the linear techniques do not produce good results. In this paper a non-linear signal-adaptive filter based on maximum likelihood (SAML) criterion is designed and successfully used for defect detection. The model of EC noise in this filter is assumed to be non-zero mean complex Gaussian process. The desired model of SAML (MSAML) filter is also modified to further reduce the probability of error and enhance defect details and boundary realization. Simulated and experimental results demonstrate the successful performance of the SAML and MASML filters in estimation of defect details and noise removal.     

An adaptive-network-based fuzzy inference system for classification of welding defects

In this paper, we describe an adaptive-network-based fuzzy inference system to recognise welding defects in radiographic images. In a first stage, image processing techniques, including noise reduction, contrast enhancement, thresholding and labelling, were implemented to help in the recognition of weld regions and the detection of weld defects. In a second stage, a set of 12 geometrical features which characterise the defect shape and orientation was proposed and extracted between defect candidates. In a third stage, an adaptive-network-based fuzzy inference system (ANFIS) for weld defect classification was used. With the aim of obtaining the best performance to automate the process of the classification of defects, of all possible combinations without repetition of the 12 features chosen, four were used as input for the ANFIS. The results were compared with the aim to know the features that allow the best classification. The correlation coefficients were determined obtaining a minimum value of 0.84. The accuracy or the proportion of the total number of predictions that were correct was determined obtaining a value of 82.6%.     

Defect localization by orthogonally projected multiple signal classification approach for magnetic flux leakage fields

Magnetic flux leakage technique is used for defect detection inside a magnetically permeable bar by measuring the leakage fields outside the bar. Defects of varying sizes in a magnetically permeable bar have been modelled as localized anti-dipoles with different moments. These defect locations and moments have to be determined based on the measurement of the leakage fields in the presence of random noise. Multiple Signal Classification (MUSIC) approach has been used to identify the defect locations and the moments of these defects. After finding the location of the first dipole representing the larger defect, using orthogonal projection of the measured magnetic field data away from the first defect location, location of the next dipole is identified by MUSIC. This process is continued until all the defects are exhausted. The leakage fields from three deeply buried defects were simulated by direct forward calculation and the resulting data were utilized for inversion using this approach. It has been possible to identify the number of defects and their locations by this approach even in the presence of reasonable levels of additive noise.     

基于FGT-FBP重构算法的焊接缺陷检测

针对焊接过程产生的缺陷,提出一种磁光成像传感的模糊灰度变换和滤波反投影(FGT-FBP)重构检测方法. 研究焊接缺陷的几何特征,通过分析裂纹和未熔合两种不同焊接缺陷在交变磁场励磁下的磁光成像特征,设计模糊规则,对磁光图像进行模糊灰度变换. 增强磁光图像对比度,使焊接缺陷形态趋势可视化,实现描述磁光成像焊接缺陷细节的无参考型图像评估方法. 对FGT处理的焊接缺陷磁光图进行旋转投影,并经过快速傅里叶变换和改进的滤波器进行滤波去噪,消除伪影后进行反投影变换实现焊接缺陷图像的重构. 利用滤波反投影重构算法进行去噪,可有效突出焊接缺陷特征. 最后结合阈值分割和边缘检测实现焊接缺陷检测. 结果表明,该方法能较准确检测裂纹和未熔合两种焊接缺陷.     

A novel approach for flip chip solder joint inspection based on pulsed phase thermography

Surface mount components have been extensively used in microelectronic packaging. However, it brings great challenge for defect inspection with the development of solder bumps towards ultra-fine pitch and high density. Traditional nondestructive detection methods are insufficient for solder joint assessment due to their own disadvantages. Therefore, it is necessary to explore new methods for solder joint inspection. A novel approach based on the pulsed phase thermography was investigated for identifying the defects of solder joints. In this approach, the test chip was stimulated by a thermal pulse, and the consequent transient response was captured by a commercial thermal imager. The spacial and temporal filtering techniques were adopted to improve the signal to noise ratio. The recorded thermograms were input to an improved median filter with a 5×5 mask, and the temperature evolution of each pixel was extracted and smoothed by the moving average operation. Then the temperature–time curve was fitted with an exponential function. To eliminate emissivity variations and heating non-uniformity, we converted the fitted temperature values in time domain to the phase information in frequency domain using the fast Fourier transform. In low frequency range, the phase–frequency curve of the defect area was differentiated from that of the sound area. The results demonstrate that this approach is effective for identification of the missing bumps, and can be used in the solder joint inspection in high density packaging.     

Discontinuities detection in welded joints based on inverse surface thresholding

Automated detection of welding defects in radiographic images becomes nontrivial when uneven illumination, contrast and noise are present. In this paper, a new approach using surface thresholding method is proposed to detect defects in radiographic images of welding joints. In the first stage, several image processing techniques namely fuzzy c means clustering, region filling, mean filtering, edge detection, Otsu thresholding, and morphological operations method are utilized to locate the area where defects might exist. This is followed by the construction of the inverse thresholding surface and its implementation to locate defects in the identified area. The proposed method was tested on 60 radiographic images and it obtained 94.6% sensitivity. Its performance is compared to that of the watershed segmentation, which obtained 69.6%.     

Imaging defects with reduced space inversion of magnetic flux leakage fields

Magnetic flux leakage technique is being used for detection of defects inside a magnetically permeable bar by measuring the leakage fields outside the bar. Defects in magnetically permeable bar have been modeled as localized anti-dipoles whose moments and locations have to be determined. Generalized inverse or pseudo-inverse technique has been used to image the defects using least-square minimum-norm solution. An initial estimate of the defects is obtained by utilizing the generalized inverse of the lead matrix over the entire region of the sample space using the measured magnetic field data including random noise. By defining a suitable threshold, and ignoring the defect locations where the moment inferred in the initial estimate was below the chosen threshold, the image of the defect is again obtained by repeating the calculation in this reduced space for the lead matrix. The leakage fields from both a line defect and a deeply buried defect were simulated and utilized for inversion using this approach. It has been possible to image the defects and quantify the strengths of the moments with the correlation coefficient approaching unity for low-noise case and attain significant improvement in the correlation coefficient for data with reasonable levels of noise.     

基于模糊聚类双谱的磁瓦内部缺陷无损检测方法

为解决磁瓦内部缺陷较难检测的问题,提出一种模糊聚类双谱分析方法用于其内部缺陷的无损检测。该方法以磁瓦在受到撞击时产生的声振信号作为研究对象,利用双谱分析发现内部缺陷与双谱峰值的分布区域具有映射关系,并且模糊聚类处理后的归一化双谱能明显地反映这一特征。根据这个规律,通过将模糊聚类双谱的对角线切片划分为若干频段,并计算切片指定幅值所在的频段建立内部缺陷识别规则。最后由验证试验评估该方法的可行性,得到了92.5%以上的识别精度。试验表明:模糊聚类双谱在磁瓦内部缺陷声振检测中具有一定实用性。     

改进的希尔伯特-黄变换(HHT)在多模式Lamb波检测中的应用

Lamb波的主要特点在于它的多模式和频散,在任一给定的激发频率下,至少存在两种Lamb波模式。各模式的频散特性使Lamb波检测变得非常复杂,所以,Lamb波检测的关键在于缺陷信号特征参数的提取和精确的信号解释。首先采用HHT直接对多模式Lamb波检测信号进行了分析,通过分析,得出了直接对多模式Lamb波信号进行EMD分解来得到信号的瞬时频率和瞬时幅值的方法是不适合的结论。采用改进后的HHT方法进行分析,有效消除了噪声干扰和各模式Lamb波的相互干扰,得到的信号时频集聚性较好,有利于特征参数的提取和进一步分析。针对不同大小和深度的孔型人工缺陷的检测试验,分别提取了信号瞬时幅值的峰值和瞬时频率的均值作为检测信号的特征参数进行线性回归分析,发现采用瞬时幅值的峰值作为孔型缺陷检测的特征参数比采用瞬时频率的均值这一参数要好。同时提出可采用瞬时幅值的峰值回归直线的斜率K来标定特征参数对缺陷孔径变化的敏感性,从而可对未知缺陷的大小进行预判。     

图像边缘提取在工件内部缺陷识别中的应用研究

工件内部缺陷是难于检验的,借助图像识别技术可以精确地确定工件内部是否存在缺陷以及界定工件内部缺陷的区域范围等,因此,断层扫描图像分割技术已广泛应用于工件内部的缺陷识别检测.为了克服采用传统分水岭算法分割图象导致的过分割现象,提出了一种图像边缘提取的智能融合算法.首先借助基于模糊形态学的开闭算法对图像做了平滑处理,其次,基于数学形态学计算了梯度算子,最后对梯度图像进行分割获得了期望的图像.仿真对比实验研究验证了该算法可较好地消除过分割现象,在工件内部缺陷图像识别中有更好的实时性与可用性.研究结果表明:提出的智能融合算法对提高图像处理质量有重要参考意义.     

Real-time automatic detection of weld defects in steel pipe

In order to detect weld defects in steel tube, a real-time imaging and detecting system is setup. In the extracted weld seam, based on spatial characteristics near defects—variance and contrast, defects such as slags, blowholes and incomplete penetration are automatically detected using the method of fuzzy pattern recognition, and the system will automatically alarm if the defect exceeds the national standard. Compared with other methods, it is simple and fast, and has fewer misinterpretations. It can detect weld defects in real-time.     

激光焊接中的等离子体变化规律及气孔缺陷快速测试方法——气孔缺陷特征参数提取方法

在激光焊接等离子体面积信号整体分析法研究的基础上，针对气孔缺陷生成时的激光小孔变化特征，继续研究了气孔缺陷与等离子体面积信号整体分布模型间的特征关联性，提出了可识别等离子体面积信号最大概率密度分布区域信号强度的基值和描述等离子体面积信号波动程度的躁动值两种特征参数的提取方法，研究了两种特征参数与实际焊接条件以及焊接气孔缺陷的关联性。结果表明，基值的大小与实际焊接条件密切相关，而躁动值的数值可较好反映出激光焊接气孔缺陷的情况。     

Automatic defect detection in ultra sonic TOFD D-scan data using image processing method

In ultrasonic time of flight diffraction (TOFD) D-scan image, only a small fraction represents defects, whereas the majority is redundant. Because of the low contrast between defect and background image, it is difficult to manually interpret TOFD image. In addition, due to the nature of the weak amplitude of ultrasonic diffracted signals, the human factor introduces inconsistency into the interpretation. In order to automatically distinguish weld defects from the D-scan image, a defect detection method based on image processing technique is proposed. First, image pre-processing including clutter and noise suppression is conducted. Second, information entropy based image segmentation technique is employed to extract defects in the pre-processed image. At last, mathematical morphology based post-processing is carried out. The experimental results show that with the proposed method, TOFD can be used for automatic weld defect detection with satisfactory level of reliability.     

激光选区熔化成形件射线检测及缺陷尺寸评价技术研究

针对TC4激光选区熔化成形件内部缺陷射线检测及定量尺寸评价,通过CR检测方法研究TC4人工缺陷（气孔、熔合不良）试样射线检测灵敏度及最小缺陷可检能力,利用显微CT定量分析缺陷尺寸、三维形貌及空间分布,并对比两种成像方法对于缺陷尺寸计量的差异。结果表明:CR检测方法能够检出?0.4 mm的模拟气孔、?0.8 mm×0.3 mm熔合不良缺陷,而显微CT具有更高的检测灵敏度,适用于微小气孔的缺陷检测、尺寸评价及定量分析,可检出?0.3 mm的模拟气孔缺陷;人工缺陷设计/实物尺寸偏差范围≤10%,并且随着缺陷设计尺寸的增大,缺陷实际打印三维形貌越不规则。     

Influences on signal/noise ratio during ultrasonic inspection of composites

This paper investigates the correlation between transducer properties, defect size and depths, and the signal/noise ratio during ultrasonic inspection of polymeric composites. Samples of different fibre/matrix systems were produced, including artificial defects of various size and shape. These specimens were inspected ultrasonically with transducers in the frequency range from 1 to 150 MHz. It could be seen that the 5 MHz transducer possessed the highest signal/noise ratio in most cases. A transducer selection catalogue incorporating special usage terms for carbon fibre/epoxy resin composites is introduced.     

无损检测模糊可靠度及缺陷模糊检出概率的分析计算

对影响无损检测可靠性的主要因素用模糊综合评判方法进行综合评判，并确定了无损检测模糊区上下限的值。在无损检测可靠度和缺陷检出概率的模糊定义基础上，将模糊集理论与概率统计分析相结合，对检测模糊可靠度和缺陷模糊检出概率进行分析计算。     

Automatic identification of different types of welding defects in radiographic images

Radiographic testing is a well-established non-destructive testing method to detect subsurface welding defects. In this paper, an automatic computer-aided identification system was implemented to recognize different types of welding defects in radiographic images. Image-processing techniques such as background subtraction and histogram thresholding were implemented to separate defects from the background. Twelve numeric features were extracted to represent each defect instance. The extracted feature values are subsequently used to classify welding flaws into different types by using two well-known classifiers: fuzzy k-nearest neighbor and multi-layer perceptron neural networks classifiers. Their performances are tested and compared using the bootstrap method.     

金属埋地管道被动式弱磁检测技术研究

在埋地金属管道检测领域中,常规检测手段多采取接触式的,检测前需要先对管道进行开挖或是停运,难以满足工业检测要求。而被动式弱磁检测技术可以实现对埋地管道的非开挖检测。本研究在介绍被动式弱磁检测技术原理的基础上,通过改变测磁传感器与管道垂直方向之间的距离来模拟管道的实际埋深,通过试验数据的分析可以得出磁场强度、管道埋深和腐蚀深度之间存在指数关系。在此基础上,结合磁场梯度变化和概率统计的原理可以得出缺陷判断的依据,即当磁场梯度在(μ-2σ,μ+2σ)区间外变化时可判定为缺陷。与常规检测手段相比,被动式弱磁检测技术无需人为对管道进行磁化。在管道非开挖条件下,可以对埋地金属管道腐蚀状况进行科学评估,并实现二维成像。