基于DR数字射线成像技术的铝合金焊缝缺陷检测

利用DR(Digital radiography)数字射线检测方法对不同透照厚度的铝合金焊缝进行射线检测试验,优化管电压、管电流、曝光时间、放大倍数等工艺参数,使其检测灵敏度满足标准要求。在此基础上分别对铝合金熔焊缝和搅拌摩擦焊缝进行数字射线检测和常规胶片射线检测对比试验,获得典型缺陷检测图像,并对缺陷的形貌分布、尺寸、性质等进行了对比分析,分析数字射线检测的缺陷检出能力,验证数字射线检测结果的可靠性。结果表明,数字射线检测灵敏度优于标准要求,且缺陷的检出能力与常规胶片射线检测基本一致,尺寸误差小于5%。     

Evaluation of polychromatic X-ray radiography defect detection limits in a sample fabricated from Hastelloy X by selective laser melting

Selective laser melting is a rapidly maturing additive manufacturing technology ideally suited to the net-shape fabrication of high value metallic components with complex shapes. However, if the processing conditions are poorly controlled, internal defects such as cracks or pores filled with metal powder may be present and impair the properties. As a result, a non-destructive defect detection method needs to be found that is suited to this application. In this work, a staircase sample was designed and fabricated from Hastelloy X by selective laser melting with step thicknesses ranging from 0.8 mm to 10 mm and with each step containing the same series of custom-made spherical, rod-shaped and coin-shaped defects arranged in different orientations and ranging from 0.2 mm up to 2 mm in size. The sample was exposed to various X-ray radiography testing and analysis methods. In particular, a theoretical and experimental evaluation of defect detection limits by polychromatic X-ray absorption radiography was performed based on the measurable contrast, which depends on both defect size and shape and slab thickness. The experimental data suggest that the minimum detectable contrast is about 1–2% when using X-rays with a very broad spectrum. This equates to a minimum detectable defect size of about 0.2 mm for a Hastelloy X slab thickness of <2 mm. The experimental findings are in good agreement with theoretical expectations. The theoretical framework provides a criterion for estimating contrast, which is useful for optimising the experimental conditions. Polychromatic X-ray absorption radiography represents a simple and effective non-destructive investigation technique. Methods for further improving the defect detection limits are also discussed and examples relative to computed tomography are reported.     

Automatic weld defect identification from radiographic images

This paper presents a new approach for weld defect identification from radiographic images. This approach is based on the generation of a database of defect features using Mel-Frequency Cepstral Coefficients (MFCCs) and polynomial coefficients extracted from the Power Density Spectra (PDSs) of the weld segmented areas after performing pre-processing and segmentation stages. Artificial Neural Networks (ANNs) are used for the feature matching process in order to automatically identify defects in radiographic images. The performance of the proposed approach is evaluated using 150 radiographic images in the presence of various types of noise and blurring. The experimental results show that the proposed approach can be used in a reliable way for automatic weld defect identification from radiographic images in noisy environments, and can achieve high recognition rates.     

焊接缺陷X射线自动检测技术研究现状

焊接生产过程中,由于各种原因,在焊接结构中会产生不同类型的缺陷,直接影响焊接质量.对国内外焊接缺陷X射线自动检测相关关键技术的研究现状给予系统的论述,包括图像增强、图像分割和模式识别,最后对这一检测技术进行了展望.     

Flaw detection in radiographic weld images using morphological approach

It is necessary to detect suspected defect regions in the radiographic weld images to find the type of flaw and its causative factors. This requires processing of radiographic images by a suitable approach. This paper presents an approach to process these radiographic weld images of the weld specimens considering morphological aspects of the image. The proposed approach first determines the flaw boundaries by applying the Canny operator after choosing an appropriate threshold value. The boundaries are then fixed using a morphological image processing approach i.e. dilating few similar boundaries and eroding some irrelevant boundaries decided on the basis of pixel characteristics. The flaws detected by this approach are categorized according to their properties.     

基于Fourier拟合曲面的X射线焊缝缺陷检测

针对在强噪声、低对比度及复杂背景特征下X射线焊缝图像的缺陷检测问题,提出了去噪处理、焊缝边缘分割及缺陷检测的方法.用快速离散Curvelet变换和循环平移相结合的方法,对焊缝图像进行滤波去噪,同时对图像列灰度曲线用最大类间方差法提取焊缝区域.在图像预处理后,采用三阶Fourier曲线对图像列灰度曲线进行拟合并扩展到三维空间,构造出自适应阈值面,最后利用原图像与构造曲面三维灰度图的灰度值差异,准确分割背景与缺陷区域.结果表明,与传统缺陷检测算法相比,该方法能准确提取出焊缝缺陷,漏检率和误判率低,准确率可达95%.     

面状缺陷超声检测图像的正演合成

为了辅助焊接缺陷超声检测的逆问题分析,通过建立仿真模型,对面状缺陷的超声检测信号进行仿真预测并对检测图像进行正演合成. 首先采用多高斯声束理论对声波在楔块、界面及被检测试件中的传播过程进行描述;其次采用Kirchhoff近似理论描述声场和面状缺陷的作用结果,制作了底面开口槽作为面状人工缺陷,并进行超声检测. 同时对人工缺陷超声检测的一维信号进行仿真预测,并正演合成二维图像. 将实际检测结果和模拟仿真结果进行比较,验证仿真模型对检测结果预测的有效性. 结果表明,正演合成图像和实际检测图像具有很好的一致性,所建立的仿真模型可辅助焊接缺陷超声检测逆问题分析.     

A guided wave approach to defect detection under shelling in rail

Rail defects are responsible for many railroad accidents. Trains are derailed and serious consequences often occur. Traditional bulk wave testing wheel probes are often inadequate for finding all defects in a rail, especially under shelling. Shelling takes place as a result of wheel to rail Hertzian contact stresses that lead to surface and subsurface defects, as a result of high stresses below the surface of the rail. Guided waves can detect shelling, if so desired, by employing the proper mode and frequency. Guided waves can also detect transverse cracking under shelling by selecting a mode and frequency insensitive to the shelling, but sensitive to transverse cracking under the shelling. Special modes and frequencies can also be used to detect defects in the web or base of a rail. The guided wave methodology of using a hybrid analytical-FEM technique to accomplish the sensor design and subsequent scattering analysis is presented in this paper. Sensor design to generate rail boundary conditions via dispersion curve and wave structure analysis is illustrated along with a few static shots of animations of wave propagation and reflection from defects in a rail.     

Empirical mode decomposition approach for defect detection in non-stationary thermal wave imaging

This paper introduces a novel empirical mode decomposition based anomaly detection in Quadratic frequency modulated thermal wave imaging. Being suited for non-stationary signal analysis, its edge over other contemporary processing modalities in its anomaly detection capability has been verified using experimentation carried over a mild steel specimen with embedded flat bottom holes. It also addresses the effect of size and depth on anomaly detection using the proposed methodology in addition to considering the signal to noise ratio of defects for detection.     

Defect detection in aircraft composites by using a neural approach in the analysis of thermographic images

Safety in aeronautics could be improved if continuous checks were guaranteed during the in-service inspection of aircraft. However, until now, the maintenance costs of so doing have proved prohibitive. For this reason there is a great interest for the development of low cost non-destructive inspection techniques that can be applied during normal routine tests. The analysis of the internal defects (not detectable by a visual inspection) of the aircraft composite materials is a difficult task unless invasive techniques are applied. In this paper, we have addressed the problem of inspecting composite materials by using automatic analysis of thermographic techniques. The analysis of the time/space variations in a sequence of thermographic images allows the identification of internal defects in composite materials that otherwise could not be detected. A neural network was trained to extract the information that characterises a range of internal defects in different types of composite materials. After the training phase the same neural network was applied to all the points of a sequence of thermographic images. The experimental results demonstrate the ability of the method to recognize regions containing defects but also to identify the contour regions that cannot be associated either with a defective or with a sound region.     

Automated detection of welding defects in pipelines from radiographic images DWDI

This paper presents a method for the automatic detection and classification of defects in radiographic images of welded joints obtained by exposure technique of double wall double image (DWDI). The proposed method locates the weld bead on the DWDI radiographic images, segments discontinuities (potential defects) in the detected weld bead and extracts features of these discontinuities. These features are used in a feed-forward multilayer perceptron (MLP) with backpropagation learning algorithm to classify descontinuities in “defect and no-defect”. The classifier reached an accuracy of 88.6% and a F-score of 87.5% for the test data. A comparison of the results with the earlier studies using SWSI and DWSI radiographic images indicates that the proposed method is promising. This work contributes towards the improvement of the automatic detection of welding defects in DWDI radiographic image which results can be used by weld inspectors as a support in the preparation of technical reports.     

The detection and sizing of flaws in components from the hot-end of gas turbines using phase-contrast radiography with neutrons: a feasibility study

In this paper we present a feasibility study of the use of phase contrast radiography in the examination of components from the hot-section of gas turbine engines. These components are usually made from dense materials (nickel or cobalt based superalloys) and, consequently, radiographic examination requires either high energy X-rays (above 60 keV) or neutrons. The relative merits of employing X-rays and neutrons for phase contrast radiography are compared. It is shown that, for similar penetration, neutrons offer better sensitivity and that it should be possible to detect even micron-wide cracks orientated perpendicular to the incident rays. Simulation shows that, for cracks parallel to the incident rays, crack growth in increments of microns can be resolved by monitoring the development of the Fresnel diffraction pattern. Some preliminary experimental results are also presented that demonstrate an improvement over conventional neutron radiography.     

Calculation of magnetic leakage field from a surface defect in a linear ferromagnetic material: an analytical approach

A novel analytical approach for calculating the magnetic leakage field from surface defects is proposed and demonstrated for the case of a linear ferromagnetic material. The novelty of the theory is that it relates the distribution of induced magnetic charges to the surface shape. An excellent agreement between the analytical and numerical results is shown. The functional relations between different magnetic field components are discussed.     

Welding with a defocused laser beam

The results of experiments carried out to investigate the welding of austenitic stainless steels using a defocused laser beam are presented. It was assumed that the application of the defocused laser beam would increase the volume of the weld pool, which in turn would reduce the requirements on the preparation of edges and the gap between the workpieces. Microhardness was measured, 2D-computer tomography carried out, specimens were examined by metallographic techniques and the phase components of different zones of the weld pool were analysed by quantitative methods. Special features of the formation of the weld pool dependent upon the welding conditions were investigated. The results indicate that the application of the defocused beam produces high-quality welded joints and the method has also a positive effect on the stability of the vapour–gas channel and the phase composition of the weld metal.     

Welding titanium with a constricted arc

The methods of similarity theory were used to study the conditions of the stable process of welding with a constricted arc of titanium and its alloys in the form of rolled sheets of different thicknesses. Taking into account the properties of titanium influencing the welding process, experiments were carried out to determine the main relationships for the selection of the optimum conditions of welding titanium and its alloys with a constricted arc.     

Progressive cutting tool wear detection from machined surface images using Voronoi tessellation method

Tool condition monitoring by machine vision approach has been gaining popularity day by day since it is a low cost and flexible method. In this paper, a tool condition monitoring technique by analysing turned surface images has been presented. The aim of this work is to apply an image texture analysis technique on turned surface images for quantitative assessment of cutting tool flank wear, progressively. A novel method by the concept of Voronoi tessellation has been applied in this study to analyse the surface texture of machined surface after the creation of Voronoi diagram. Two texture features, namely, number of polygons with zero cross moment and total void area of Voronoi diagram of machined surface images have been extracted. A correlation study between measured flank wear and extracted texture features has been done for depicting the tool flank wear. It has been found that number of polygons with zero cross moment has better linear relationship with tool flank wear than that of total void area.     

An image detection approach to NC rough-cut milling from solid models

This paper presents a method of converting information from a CAD solid model into a form suitable for CAM operation. The rough machining of a cavity into a block is used to illustrate the working of the system. This example was chosen because the machining of cavities presents difficulties as outlined below and rough machining involves the removal of more material than finish machining and therefore is of more economic significance in a production process. A three-axis NC miller is used to cut the cavity which has depth, requiring material to be removed in layers. A computer-based image detection method is used for cutter-path generation and models that contain planar surfaces, general quadratic surfaces, B-spline surfaces and compound surfaces can be treated. The B-rep solid object is transformed into an image as a grid-height model, allowing a three-dimensional object to be approximated by a two-dimensional spatial array. The cutter location (CL) data file is automatically generated from this spatial array. The height change of stock material in each grid is recorded in a two-dimensional array during the machining process and is utilized as an image for further roughing and verification. Efficient machining procedures are obtained by an analysis of cutting simulation, utilizing large cutter sizes for simple shaped portions in the first stage of roughing and then using small cutter sizes for complex portions and to remove uncut material left by the large cutters in a fine roughing operation. This approach allows a three-dimensional cutter path problem to be reduced to one of two dimensions which is solved by image detection of cutting attributes.     

Modeling guided wave propagation with application to the long-range defect detection in railroad tracks

The aim of this work is to demonstrate the use of a commercial finite element package, ABAQUS EXPLICIT, to model ultrasonic guided waves in structural components. The particular application of interest is modeling the interaction of a broadband vertical bending mode with transverse-type defects in railroad tracks. This topic is part of a broader project on high-speed defect detection in rails by long-range ultrasonic inspections. Reflection coefficient spectra in the 20–45 kHz range are obtained for four different sizes and three different orientations of transverse head flaws. A preliminary study of Lamb waves in a free plate helps drawing modeling guidelines for the rail.     

Real-time arc-welding defect detection and classification with principal component analysis and artificial neural networks

A novel system which allows arc-welding defect detection and classification is presented in this paper. The spectroscopic analysis of the plasma spectra produced during the welding process is a well-known technique to monitor the quality of the resulting weld seams. The analysis of specific emission lines and the subsequent estimation of the electronic temperature T_e profile offers a direct correlation between this parameter and the corresponding weld seams. However, the automatic identification and classification of weld defects has proven to be difficult, and it is usually performed by means of statistical studies of the electronic temperature profile. In this paper, a new approach that allows automatic weld defect detection and classification based in the combined use of principal component analysis (PCA) and an artificial neural network (ANN) is proposed. The plasma spectra captured from the welding process is processed with PCA, which reduces the processing complexity, by performing a data compression in the spectral dimension. The designed ANN, after the selection of a proper data training set, allows automatic detection of weld defects. The proposed technique has been successfully checked. Arc-weld tests on stainless steel are reported, showing a good correlation between the ANN outputs and the classical interpretation of the electronic temperature profile.