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.

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.

Automatic Detection of Weld Defects in Pressure Vessel X-Ray Image Based on CNN

The visual automatic detection method based on artificial intelligence has attracted more and more attention. In order to improve the performance of weld nondestructive defect detection,we propose DRepDet(Dilated RepPoints Detector). First, we analyze the weld defect dataset in detail and summarize the distribution characteristics of weld defect data, that is, the defect scale is very different and the aspect ratio distribution range is large. Second, according to the distribution characteristics of defect data, we design DResBlock module, and introduce dilated convolution with different dilated rates in the process of feature extraction to expand the receptive field and improve the detection performance of large-scale defects. Based on DResBlock and anchor-free detection framework RepPoints, we design DRepDet. Extensive experiments show that our proposed detector can detect 7 types of defects. When using combined dilated rate convolution network in detection, the AP50 and Recall50 of big defects are improved by 3.1% and 3.3% respectively, while the performance of small defects is not affected, almost the same or slightly improved. The final performance of the whole network is improved a large margin,with 6% AP50 and 4.2% Recall50 compared with Cascade RCNN and 1.4% AP50 and 2.9% Recall50 compared with RepPoints.

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.

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.

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.

Investigation on the process parameters of TIG-welded aluminum alloy through mechanical and microstructural characterization

Multi-pass TIG welding was conducted on plates(15×300×180 mm^(3))of aluminum alloy Al-5083 that usually serves as the component material in structural applications such as cryogenics and chemical processing industries.Porosity formation and solidification cracking are the most common defects when TIG welding Al-5083 alloy,which is sensitive to the welding heat input.In the experiment,the heat input was varied from 0.89 kJ/mm to 5 kJ/mm designed by the combination of welding torch travel speed and welding current.Tensile,micro-Vicker hardness and Charpy impact tests were executed to witness the impetus response of heat input on the mechanical properties of the joints.Radiographic inspection was performed to assess the joint’s quality and welding defects.The results show that all the specimens displayed inferior mechanical properties as compared to the base alloy.It was established that porosity was progressively abridged by the increase of heat input.The results also clinched that the use of medium heat input(1-2 kJ/mm)offered the best mechanical properties by eradicating welding defects,in which only about 18.26% of strength was lost.The yield strength of all the welded specimens remained unaffected indica ted no influence of heat input.Partially melted zone(PMZ)width also affected by heat input,which became widened with the increase of heat input.The grain size of PMZ was found to be coarser than the respective grain size in the fusion zone.Charpy impact testing revealed that the absorbed energy by low heat input specimen(welded at high speed)was greater than that of high heat input(welded at low speed)because of low porosity and the formation of equiaxed grains which induce better impact toughness.Cryogenic(-196℃)impact testing was also performed and the results corroborate that impact properties under the cryogenic environment revealed no appreciable change after welding at designated heat input.Finally,Macro and micro fractured surfaces of tensile and impact specimens were analyzed using Stereo and Scanning Electron Microscopy(SEM),which have supported the experimental findings.

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.

Contrast Correction Using Hybrid Statistical Enhancement on Weld Defect Images

Luminosity and contrast variation problems are among the most challenging tasks in the image processing field,significantly improving image quality.Enhancement is implemented by adjusting the dark or bright intensity to improve the quality of the images and increase the segmentation performance.Recently,numerous methods had been proposed to normalise the luminosity and contrast variation.A new approach based on a direct technique using statistical data known as Hybrid Statistical Enhancement(HSE)is presented in this study.TheHSE method uses themean and standard deviation of a local and global neighbourhood and classified the pixel into three groups;the foreground,border,and problematic region(contrast&luminosity).The datasets,namely weld defect images,were utilised to demonstrate the effectiveness of the HSE method.The results from the visual and objective aspects showed that the HSE method could normalise the luminosity and enhance the contrast variation problem effectively.The proposed method was compared to the two(2)populor enhancement methods which is Homomorphic Filter(HF)and Difference of Gaussian(DoG).To prove the HSE effectiveness,a few image quality assessments were presented,and the results were discussed.The HSE method achieved a better result compared to the other methods,which are Signal Noise Ratio(8.920),Standard Deviation(18.588)and Absolute Mean Brightness Error(9.356).In conclusion,implementing the HSE method has produced an effective and efficient result for background correction and quality images improvement.

Impact property analysis of weld metal and heat-affected zone for low-alloy carbon steel multi-pass welded joint

Welded joint impact performances of low-alloy carbon steel plates welded by full-automatic gas metal arc welding （GMAW） were evaluated. To clarity the effect of impact temperature on impact properties of weld metal （WM） and heat- affected zone （ HAZ）, Charpy V impact tests at different temperatures and fracture surface analysis were carried out. The Charpy V impact energy decreases with the decreasing test temperature both for the WM and HAZ, while the proportion of crystal zone on WM and HAZ impact fracture surface increases with the decreasing test temperature. Research results indicate that the welding defects （void and slag） make the impact energy of WM more scattered and lower than that of HAZ.

ON LINE MONITORING OF BURNING THROUGH FOR SHORT CIRCUIT CO_2 ARC WELDING BASED ON THE SELF-ORGANIZE FEATURE MAP NEURAL NETWORKS

A method for automatic detection of burning through of short circuit CO 2 arc welding is presented. It is based on the extraction of arc signal features as well as classification of the obtained features using self organize feature map(SOM) neural networks in order to get the weld quality information, for example, to determine if there is defect in the product. This is important for the on line monitoring of weld quality especially in robotic welding and lay the foundation for the further real time control of weld quality.

Numerical analysis and experimental research on detection of welding defects in pipelines based on magnetic flux leakage

Regular inspection of long-distance oil and gas pipelines plays an important role in ensuring the safe transportation of oil and gas,and inspection on welding defects is an important part of the inspection process.Magnetic flux leakage(MFL)is an electromagnetic non-destructive testing technique which has been commonly utilized to detect welding defects in pipelines.In the present study,Maxwell electro-magnetic simulation software was used to carry out numerical study on the welding defects in pipelines,including incomplete penetration and undercut.TheФ406 pipeline with a wall thickness of 7 mm was selected as the study case to establish the numerical model.Setting the life-off value at 1 mm,the distribution of magnetic leakage field was investigated for pipeline without defect,pipeline with incomplete penetration defect and pipeline with undercut defect respectively,the characteristic values describing the depth and width of defects were found.Furthermore,quantified equations which can be used to describe the defect depth were proposed.Finally,experimental research was carried out to validate the effectiveness of the numerical model,and the experimental results showed good consistence with the numerical calculation results.The research results indicate that,it is technically feasible and reliable to diagnose the incomplete penetration and undercut welding defects in pipelines using MFL.

Expert system for evaluating the safety of pressure vessels

With more application of welding technology in important structures more attention was paid to the evaluation of the safety of welded structures, the life prediction and decision to repair the welded structures. Based on material fracture mechanism and Chinese standard of safety evaluations of pressure vessels, an expert system was developed to evaluate the safety of welded pressure vessels. The system can analyze the weld defects in a pressure vessel, convert different kinds of defects into equivalent cracks and obtain their equivalent sizes. Furthermore, the system can calculate the stress and strain in the positions of weld defects and make decision on whether the defects are tolerable or not according to the code. When it is tolerable, the system will calculate the safety margin. The fatigue life can be predicted if the defects undergo fatigue load too. Moreover, data bases are built for storing mechanical properties of material and evaluated results.

Plastic limit load analysis for pressure pipe with incomplete welding defects based on the extended Net Section Collapse Criteria

With von Mises yield criterion,the loading range of Net Section Collapse(NSC) Criteria is extended from combined tension and bending loadings to combined bending,torsion and internal pressure loadings.A new theoretical analyzing method of plastic limit load for pressure pipe with incomplete welding defects based on the extended NSC Criteria is presented and the correlative formulas are deduced,the influences of pipe curvature,circumferential length and depth of incomplete welding defects on the plastic limit load of pressure pipe are considered as well in this method.Meanwhile,according to the orthogonal experimental design method,the plastic limit loads are calculated by the finite element method and compared with the theoretical values.The results show that the expressions of plastic limit load of pressure pipe with incomplete welding defects under bending,torsion and internal pressure based on extended NSC criteria are reliable.The study provides an important theoretical basis for the establishment of safety assessment measure towards pressure pipe with incomplete welding defects.

Failure analysis of crevice corrosion on 304 stainless steel tube heat exchanger

The cause of the premature failure of 304 stainless steel tube heat exchanger was investigated.The unique skeleton structure inside the leakage point reveals that this is a new damage mechanism caused by a δ+γ two-phase structure and crevice corrosion.The three-dimensional structure of the leakage point was demonstrated using X-ray diffraction topography.The results of scanning electron microscope examination show the microstructure of the weld to be columnar and dendritic.It is found by electron probe microscope analysis and transmission electron microscopy that columnar dendrites consisted of γ-dendrite and an amount of δ-ferrite phases at the dendrite trunk.Simulated corrosion test results confirmed that the corrosion medium was the chloride ion.Crevice corrosion of chloride ions occurred at weld defects on the inner wall thus forming a concentration cell.Grains of columnar dendrites were then corroded by chloride ions and δ-ferrite phases on the grain boundaries were retained,which formed the particular skeleton corrosion structure.As a result,it led to leakage when the corrosion of weld occurred from the inner wall to the outer wall.

Automatic Defect Detection in X-Ray Images Using Image Data Fusion

Automatic defect detection in X-ray images is currently a focus of much research at home and abroad. The technology requires computerized image processing, image analysis, and pattern recognition. This paper describes an image processing method for automatic defect detection using image data fusion which synthesizes several methods including edge extraction, wave profile analyses, segmentation with dynamic threshold, and weld district extraction. Test results show that defects that induce an abrupt change over a predefined extent of the image intensity can be segmented regardless of the number, location, shape or size. Thus, the method is more robust and practical than the current methods using only one method.