A fast and adaptive method for automatic weld defect detection in various real-time X-ray imaging systems

A first and effective method is proposed to detect weld deject adaptively in various Dypes of real-time X-ray images obtained in different conditions. After weld extraction and noise reduction, a proper template of median filter is used to estimate the weld background. After the weld background is subtracted from the original image, an adaptite threshold segmentation algorithm is proposed to obtain the binary image, and then the morphological close and open operation, labeling algorithm and fids＇e alarm eliminating algorithm are applied to pracess the binary image to obtain the defect, ct detection result. At last, a fast realization procedure jbr proposed method is developed. The proposed method is tested in real-time X-ray image,s obtairted in different X-ray imaging sutems. Experiment results show that the proposed method is effective to detect low contrast weld dejects with few .false alarms and is adaptive to various types of real-time X-ray imaging systems.

Automated X-ray Defect Inspection on Occluded BGA Balls Using Hybrid Algorithm

Automated X-ray defect inspection of occluded objects has been an essential topic in semiconductors,autonomous vehicles,and artificial intelligence devices.However,there are few solutions to segment occluded objects in the X-ray inspection efficiently.In particular,in the Ball Grid Array inspection of X-ray images,it is difficult to accurately segment the regions of occluded solder balls and detect defects inside solder balls.In this paper,we present a novel automatic inspection algorithm that segments solder balls,and detects defects fast and efficiently when solder balls are occluded.The proposed algorithm consists of two stages.In the first stage,the defective candidates or defects are determined through the following four steps:(i)image preprocessing such as noise removal,contrast enhancement,binarization,connected component,and morphology,(ii)limiting the inspec-tion area to the ball regions and determining if the ball regions are occluded,(iii)segmenting each ball region into one or more regions with similar gray values,and(iv)determining whether there are defects or defective candidates in the regions using a weighted sum of local threshold on local variance.If there are defective candidates,the determination of defects is finally made in the following stage.In the second stage,defects are detected using the automated inspection technique based on oblique computed tomography.The 3D precision inspection process is divided into four steps:(i)obtaining 360 projection images(one image per degree)rotating the object from 0 to 360 degrees,(ii)reconstructing a 3D image from the 360 projected images,(iii)finding the center slice of gravity for solder balls from the axial slice images in the z-direction,and getting the inspection intervals between the upper bound and the lower bound from the center slice,and(iv)finally determining whether there are defects in the averaged image of solder balls.The proposed hybrid algorithm is robust for segmenting the defects inside occluded solder balls,and improves the performance of solder ball segmentation and defect detection algorithm.Experimental results show an accuracy of more than 97%.

FEATURE ANALYSIS AND EXTRACTION OF INNER DEFECTS FOR SPOT WELD NUGGET OF ALUMINUM ALLOY

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Image segmentation of small defects in precision weldments with complex structure

In this paper, the X-ray nondestructive test method of small defects in precision weldments with complex structure was presented. To resolve the difficulty of defect segmentation in variable grey image, the image processing based on Visual Basic programming method was adopted. The methods of automatic contrast and partial grey stretch were used to enhance the X-ray detection image which has relatively low contrast, then automatic threshold method was carried out to segment the two high intensity zones, and weld zones which contain the small defects was extracted. Smoothing and sharpen processing were proceeded on the extracted weld zones, and small defects in X-ray detection image of weldments with complex structure were segmented by using the method of background subtraction in the end. The effects of raster were eliminated, and because of that the image processing was only proceeded on the extracted weld zones, the calculated speed using the above provided algorithm was improved.

Multiaxial Fatigue Analysis on Reeled Deepwater Steel Catenary Risers with Girth Weld Defects

In the present study, we simulated the reel-lay installation process of deepwater steel catenary risers（SCRs） using the finite element method and proposed multiaxial fatigue analysis for reeled SCRs. The reel-lay method is one of the most efficient and economical pipeline installation methods. However, material properties of reeled risers may change, especially in the weld zone, which can affect the fatigue performance. Applying finite element analysis（FEA）, we simulated an installation load history through the reel, aligner, and straightener and analyzed the property variations. The impact of weld defects during the installation process, lack of penetration and lack of fusion, was also discussed. Based on the FEA results, we used the Brown-Miller criterion combined with the critical plane approach to predict the fatigue life of reeled and non-reeled models. The results indicated that a weld defect has a significant influence on the material properties of a riser, and the reel-lay method can significantly reduce the fatigue life of SCRs. The analysis conclusion can help designers understand the mechanical performance of welds during reel-lay installation.

Application of Support Vector Machine in Weld Defect Detection and Recognition of X-ray Images

Support vector machines（SVM） received wide attention for its excellent ability to learn, it has been applied in many fields. A review of the application of SVM in weld defect detection and recognition of X-ray image is been presented. We will show some commonly used methods of weld defect detection and recognition using SVM, and the advantages and disadvantages of each method will be discussed. SVM appears to be promising in weld defect detection and recognition, but future research is needed before it fully mature in this filed.

Acoustic Emission Studies on Weld Bead Defects in Nuclear Grade SS 316L Materials

This paper contributes about the behaviour of Acoustic Emission (AE) signatures of implanted weld defects of SS 316L materials. Lack of penetration and lack of side fusion defects were implanted in weld bead region of the materials. Tungsten Inert Gas Welding (TIG) is adopted to weld the Stainless Steel (SS316L) nuclear grade materials. The material is fabricated with dimensions of 140 × 16 × 10 mm and AE signatures are studied under preload conditions. Mechanical Jig is fabricated to maintain constant load in concentrated weld region. When external load is applied on the weld region, the deformed specimen experiences acoustic emission signals form the weld defect region which are potential source of releasing stress energy. Liner Location Technique (LLT) is adopted for AE singal studies and the generated signal is processed by 2-channel USB—AE node and AE-WIN software. The tests are conducted on two different samples having each defect. A conventional NDT method i.e. X-ray Radiography is conducted on the samples to know the defect ranging and correlated with AE signatures. This study will be helpful to standardize the AE signals for different implanted weld defects of SS 316L materials and it is found that, the parameter “counts vs. amplitude” has given the widest distinction with respect to the type of defects.

Study on intelligent image welding defects in recognition for the tube pressure vessels

Currently, the welding defects recognition of X-ray nondestructive inspection is principally carried out by manual work, which highly depends on the experience of the inspectors and costs plenty of workload. In this paper, an intelligent image processing and recognition method for the tube welding radiographic testing in large-scale pressure vessels is proposed. Firstly, the raw image is preprocessed by median filtering, pseudo point removing and non-lincar image enhancement. Secondly, the welded joints parts are separated from the whole image by edge detection and threshold segmentation algorithms. Then, the separated images are handled by FFT transformation. Finally, whether defects exist and the specific type of defects are judged by Support Vector Machine. Software developed basing on this method works stably on site, and experiments demonstrate that the recognition results are compliance with the JB/T 4730. 2 or ASME standards.

Defects of friction stir welded 1060 aluminum alloy and their effects on joint tensile property

9. 6 mm thick 1060-H24 aluminum alloy plates were friction stir welded and the influencing factors on groove and tunnel defects were examined. Results show that the welding speed range for achieving a groove-free joint is enlarged with increasing the rotating speed. The tunnel size decreases with decreasing the welding speed under the same rotating speed. Excessive or insufficient shoulder plunge depth will cause defective joints. At a relatively low rotating speed and high welding speed, the tool having a larger shoulder diameter has a larger range of processing parameters to obtain a groove-free joint. Moreover, the tensile fracture behaviors of the defective and defect-free samples are different.

Classified recognition for metal magnetic memory signals of welding defects in API 5L X65 pipeline steel

Feature extraction and selection from signals is a key issue for metal magnetic memory testing technique. In order to realize the classification of metal magnetic memory signals of welding defects, four fractal analysis methods, such as box- counting, detrended fluctuation, minimal cover and rescaled-range analysis, were used to extract the feature signal after the original metal magnet memory signal was de-noising and differential processing, then the Karhunen-Lo^e transformation was adopted as classification tool to identify the defect signals. The result shows that this study can provide an efficient classification method for metal magnetic memory signal of welding defects.

X-ray ptychographic tomography reveals buried 3D structural defects in metal halide perovskites

Metal halide perovskites(MHPs)are an emerging class of semiconductors that have demonstrated their promise at various energy frontiers.Especially,perovskite-based solar cells(PSCs)are considered as a disruptive photovoltaic technology with their power conversion efficiency rapidly climbing to certified 25.7%[1].

Automated identification of steel weld defects,a convolutional neural network improved machine learning approach

This paper proposes a machine-learning-based methodology to automatically classify different types of steel weld defects,including lack of the fusion,porosity,slag inclusion,and the qualified(no defects)cases.This methodology solves the shortcomings of existing detection methods,such as expensive equipment,complicated operation and inability to detect internal defects.The study first collected percussed data from welded steel members with or without weld defects.Then,three methods,the Mel frequency cepstral coefficients,short-time Fourier transform(STFT),and continuous wavelet transform were implemented and compared to explore the most appropriate features for classification of weld statuses.Classic and convolutional neural network-enhanced algorithms were used to classify,the extracted features.Furthermore,experiments were designed and performed to validate the proposed method.Results showed that STFT achieved higher accuracies(up to 96.63%on average)in the weld status classification.The convolutional neural network-enhanced support vector machine(SVM)outperformed six other algorithms with an average accuracy of 95.8%.In addition,random forest and SVM were efficient approaches with a balanced trade-off between the accuracies and the computational efforts.

Feature extraction of welding defect based on machine vision

There are many flaws in welding images such as noise, low contrast, and blurred edges, which affect feature extraction from welding defect regions and impede classification and recognition of welding defects. To deal with the complexity of welding defect images, this paper proposes an effective method for extracting the features of welding defect regions. Firstly, image preprocessing, image segmentation and image background removal are carried out to a welding image in order to extract welding defect region; and then an 8-connected-component labeling method is used to mark defect regions. Finally, it extracts geometric characteristic parameters including perimeter, area, circularity and others. The experimental result shows that the method proposed in the paper can accurately extract the features of welding defect regions. It has good adaptability and practicability.

Studies on the effect of weld defect on the fatigue behavior of welded structures

Structural integrity isstated as the science and technology of margin between safety and disaster. Systematic prediction of structural integrity of critical structures such ascombustion chambers,pressure vessels,nuclear reactor components,boilers etc.,ensures the human safety,environmental protection,and the economical considerations.The present work aims at prediction of fatigue behaviour of symmetric structures like pressure vessels in the presence of common welding defects such as lack of fusion( LOF),lack of penetration( LOP) and porosity.A ring type specimen which replicates the stress pattern in thepressure vessel is considered for the study of severity of weld imperfections. Initial dimensions of weld defects are arrived by performing NDT inspection.Crack growth analysis is carried out to determine the remaining life of the welded joint with defects.

Enlarged-end tool for friction stir lap welding towards hook defect controlling

Hook defect is one of the trickiest issues for friction stir lap welding,which remains to be resolved.In this study,a designed pin with an enlarged-end was proposed to control the interfacial hook defect by optimizing its morphology orientation.The insert aduncous structure at the advancing side is dissected by multilayer metallographic observation,which is the main character to terminate the inner-ward extension of the hook.The application of this tool was verified practical with a varied plunge depth of pin into the lower plate from 0.1 mm to 1.6 mm.The angle of the hook was small and terminated at the insert-structure or oriented toward the material convergence region.No joint fractured along the hook line and the highest joint efficiency reached 86%of the parent 6082-T4 aluminum alloy.

Study of Crystal Defects and Spectroscopy Characteristics of Ammonium Polyphosphate

Two kinds of commercial ammonium polyphosphate （APP） and three kinds of APP which were prepared in the laboratory were studied by X-ray diffraction （XRD）, Fourier transforms infrared spectroscopy （FTIR）, scanning electron microscopy （SEM） and transmission electron microscopy （TEM）. In identification of the form Ⅱ crystal APP by XRD and FTIR, some discrepancies were discussed. It is pointed out that the absorbance of the FTIR spectra at 682 cm^-1 can exist not only in the form Ⅰ APP, but also in the form Ⅱ APP with the crystal lattice defects. The SEM images indicate that the form Ⅱ APP is of multilayer crystal structure. XRD and TEM can reveal the crystal lattice defects.

Effect of undercut defect on deformation behavior TC4 titanium alloy laser welded butt joint under static tensile loading

By measuring and analyzing infrared thermal image of the specimen in static load tensile test process, it was studied that the influence of the undercut defects and double-sided dressing method on the deformation behavior of the laser welded joint specimens of TC4 titanium alloy. The results showed that for the unmodified specimens, the yield phenomenon occurs first in the region of the joint, but the undercut value has an effect on the stress and strain of starting to yield phenomenon, and a great effect on the plastic deformation behavior.When the undercut is less than a certain value, the large plastic deformation occurs in the base metal region and the plasticity of the specimen is comparable to that of the base metal, but the larger undercut defect results in a concentrated plastic deformation in the joint region and rapidly failed in this region. But the double-sided dressing specimen is significantly different. The physical yield is no longer concentrated in the joint region, but at the same time occurs in the several regions including joint and the base metal. And the plastic deformation mainly occurs in the base material area, similar to that of the base material.

Formative mechanism of the undercut defect in horizontal variable polarity plasma arc welding

Abstract Horizontal welding is important for heavy or huge welding structures. Keyhole mode variable polarity plasma arc welding of aluminum alloy plates with medium thickness was carried out in horizontal position. The characteristic of welding defects was introduced. Preliminary experiments indicated that the undercut defect could not be eliminated easily. The relationship between welding parameters and the undercut defect showed that this deject could be lessened by using higher heat input. The fluid flow of weld pool was observed by a high speed camera. The fluid flow in weld pool was not symmetric and much of molten metal gathered in the lower part. The fluid flow velocity in the lower part was bigger than that in the upper part. To this end, the formative mechanism of the undercut defect was proposed. The flowability of the molten metal was an influential factor for the undercut defect. A preheating method was designed to verify the formative mechanism.

Repair welding process of friction stir welding groove defect

The groove defect formed in the friction stir welding dramatically deteriorates weld appearances and mechanical properties of the joints owing to its larger size and penetration. Therefore, the friction stir repair welding was utilized to remove such a groove defect, and the focus was placed on the mechanical properties and microstructural characteristics of the repair joints so as to obtain an optimum repair welding process. The experimental results indicate that the groove defect can be removed by friction stir repair welding, and the offset repair welding process is superior to the symmetrical repair welding process. In the symmetrical repair welding process, a large number of fine cavity defects and an obvious aggregation of hard-brittle phase Al2Cu occur, accordingly the mechanical properties of the repair joint are weakened, and the fracture feature of repair joint is partially brittle and partially plastic. A good-quality repair joint can be obtained by the offset repair welding process, and the repair joint is fractured near the interface between the weld nugget zone and thermal-mechanically affected zone.

Noise Reduction of Welding Defect Image Based on NSCT and Anisotropic Diffusion

In order to reduce noise effectively in the welding defect image and preserve the minutiae information, a noise reduction method of welding defect image based on nonsubsampled contourlet transform(NSCT) and anisotropic diffusion is proposed. Firstly, an X-ray welding defect image is decomposed by NSCT. Then total variation(TV) model and Catte_PM model are used for the obtained low-pass component and band-pass components, respectively. Finally, the denoised image is synthesized by inverse NSCT. Experimental results show that, compared with the hybrid method of wavelet threshold shrinkage with TV diffusion, the method combining NSCT with P_Laplace diffusion, and the method combining contourlet with TV model and adaptive contrast diffusion, the proposed method has a great improvement in the aspects of subjective visual effect, peak signal-to-noise ratio(PSNR) and mean-square error(MSE). Noise is suppressed more effectively and the minutiae information is preserved better in the image.