Pipeline Welding Research Articles

Development of a technique for applying pa and tofd methods for mechanized ultrasonic testing of welded joints of main pipelines using the USD-60FR-16/128 flaw detector

A technique was developed and tested for selecting beam steering and focusing methods for mechanized ultrasonic testing of circumferential welds of pipelines up to 16 mm thick using phased antenna arrays. Criteria for the identification and interpretation of defects on the time-of-flight diffraction scans in the form of an atlas of defects were developed. The study was carried out using a new domestic flaw detector USD-60FR-16/128.

Research on magnetic memory inspection signal characteristics of multi-parameter coupling pipeline welds

Magnetic memory inspection technology is an effective method for detecting early damage in ferromagnetic materials. However, the commonly used force-magnetic coupling models are insufficient for analyzing the distribution characteristics of magnetic signals in ferromagnetic material welds. This study first analyzes the influence of metal phase transformation and residual stress on the magnetic hysteresis characteristics of materials. Based on the principle of magnetic hysteresis characteristics, the magnetic charge density under the combined action of stress and phase transformation is determined, and a magnetic memory analysis model is established for multi-parameter coupling. The study quantitatively calculates the effects of phase transformation volume fraction, residual stress, the lift-off effect, and cracks on the magnetic signal features at welds. The research results show that the magnetic memory signals at welds exhibit extreme values in the tangential component and sinusoidal fluctuations with zero-crossings in the normal component. The peak-to-peak values of both the normal and tangential components of the magnetic signal increase with an increase in metal phase transformation and residual stress, while they decrease with the increase of the lift-off effect. When cracks appear in the weld, the normal component exhibits a double-peak and valley structure, with the inner side representing crack features and the outer side representing weld features. The tangential component shows a maximum value at the center of the crack. Based on the peak and valley values of the magnetic signal, a damage grade index F is determined. The damage index F can characterize the level of weld damage, decreasing approximately linearly with increasing stress.

Fuzzy control of backside weld width in cold metal transfer welding of X65 pipeline in the vertical-up position

Fuzzy control of backside weld width in cold metal transfer welding of X65 pipeline in the vertical-up position

Online monitoring and penetration recognition in all-position TIG welding of nuclear power pipeline

In the construction of a nuclear island, a nuclear power pipeline is the “aorta” of the nuclear power plant, which performs the critical role of a nuclear safety protection barrier. By automating welding, nuclear power pipeline can maintain high quality and reduce the need for skilled welders. We developed an all-position TIG welding quality monitoring system to address the low internal forming quality assessment accuracy for nuclear power pipeline welding. An algorithm for data processing extracted the features of the pipeline groove, back molten pool, and solidified weld via sensors mounted on the internal crawler mechanism. Based on the process parameters collected during welding, a penetration state recognition model was constructed using a random forest. The results show that, compared with the decision tree and support vector machine, the random forest model has an accurate penetration recognition performance and can realize automatic assessment of pipeline welding quality.

Interpretable real-time monitoring of pipeline weld crack leakage based on wavelet multi-kernel network

The deep learning technology used for pipeline weld crack leakage monitoring lacks physical interpretability, which makes it difficult to provide theoretical support to decision-makers, reducing the practicality and applicability of the technology. To solve this problem, a deep learning framework that combines interpretability and feature fusion is proposed to solve the real-time monitoring problem of pipeline leaks. First, three different wavelet basis functions are used to analyze the leakage acoustic emission signal, and the resulting time-frequency features exhibit correlation and complementarity. Based on this, an interpretable neural network with different wavelet convolution kernels is designed to extract abstract feature details of wideband acoustic emission signals through multi-level dynamic receptive fields. Then, a feature fusion module based on channel importance weighting was designed to optimize the learning process of the network by highlighting the contribution of different channels. Finally, the effectiveness and superiority of the proposed method were verified through pipeline leakage acoustic emission monitoring experiments. The results show that the proposed method can effectively extract distinguishing features of leak acoustic emission signals, and the recognition accuracy of different leakage states can reach 98.32%, which is significantly higher than that of typical deep learning methods. In addition, feature map visualization proves that the proposed method has physical interpretability in abstract feature extraction, which will provide a reference basis for correct decision-making by industrial technicians.

COMPARATIVE ANALYSIS AND SIMULATION OF WELDING MODES DURING THE CONSTRUCTION OF THE MAIN PIPELINE USING AUTOMATED TECHNOLOGIES

In the present work, the technology of laser welding of the circumferential joints of pipes of the main gas pipeline was studied on the basis of the existing certain experience of its application at the facilities of the gas transmission system and an assessment was made of the possibility of using it as an alternative to automatic arc welding during construction and repair and restoration work. An analysis was made of the use of laser radiation for orbital welding of main pipelines, which showed that the use of this technology makes it possible to increase the productivity of the welding process, to obtain more favorable structures in the seam and in the transition zone, and to ensure a high level of mechanical properties and weld density. Temperature and deformation conditions in the zone of the welded joint help to reduce the likelihood of hot and cold cracking.Modeling of a pipeline section with welded joints made by automatic arc welding and laser welding (hereinafter referred to as LS) was carried out. When modeling welded joints, their geometric parameters for the compared welding technologies were taken into account, as well as the area and volume of the deposited metal, which theoretically correspond to natural values. Loads were applied to the pipe section with a welded seam, imitating three types of mechanical tests: tension, compression and bending, which occur in the case of thermal deformations and soil subsidence during pipeline operation.In the course of the study, stresses were determined in the cross section of a welded joint obtained using two welding technologies, which show the level of mechanical properties of the weld. A comparative analysis and evaluation of the operational reliability of the main gas pipeline with welded joints made by modern automated welding technologies has been carried out.

Research Progress and Prospects on Hydrogen Damage in Welds of Hydrogen-Blended Natural Gas Pipelines

Hydrogen energy represents a crucial pathway towards achieving carbon neutrality and is a pivotal facet of future strategic emerging industries. The safe and efficient transportation of hydrogen is a key link in the entire chain development of the hydrogen energy industry’s “production, storage, and transportation”. Mixing hydrogen into natural gas pipelines for transportation is the potential best way to achieve large-scale, long-distance, safe, and efficient hydrogen transportation. Welds are identified as the vulnerable points in natural gas pipelines, and compatibility between hydrogen-doped natural gas and existing pipeline welds is a critical technical challenge that affects the global-scale transportation of hydrogen energy. Therefore, this article systematically discusses the construction and weld characteristics of hydrogen-doped natural gas pipelines, the research status of hydrogen damage mechanism, and mechanical property strengthening methods of hydrogen-doped natural gas pipeline welds, and points out the future development direction of hydrogen damage mechanism research in hydrogen-doped natural gas pipeline welds. The research results show that: ① Currently, there is a need for comprehensive research on the degradation of mechanical properties in welds made from typical pipe materials on a global scale. It is imperative to systematically elucidate the mechanism of mechanical property degradation due to conventional and hydrogen-induced damage in welds of high-pressure hydrogen-doped natural gas pipelines worldwide. ② The deterioration of mechanical properties in welds of hydrogen-doped natural gas pipelines is influenced by various components, including hydrogen, carbon dioxide, and nitrogen. It is necessary to reveal the mechanism of mechanical property deterioration of pipeline welds under the joint participation of multiple damage mechanisms under multi-component gas conditions. ③ Establishing a fundamental database of mechanical properties for typical pipeline steel materials under hydrogen-doped natural gas conditions globally is imperative, to form a method for strengthening the mechanical properties of typical high-pressure hydrogen-doped natural gas pipeline welds. ④ It is essential to promptly develop relevant standards for hydrogen blending transportation, welding technology, as well as weld evaluation, testing, and repair procedures for natural gas pipelines.

Defect identification method for ultrasonic inspection of pipeline welds based on cross-modal zero-shot learning

Ultrasonic inspection of pipeline welds still uses the traditional visual inspection signal method to identify pipeline defects. The identification of defects relies entirely on the subjective judgment of practitioners and is highly dependent on their level of experience. Deep learning models have achieved very good results in classification tasks, but they rely on a large number of annotated data samples for each category. However, it is difficult to collect a large number of samples with different defects and annotate them for the classification of pipe welding defects. Based on the idea of zero-shot learning (ZSL), which makes full use of experts’ semantic descriptions of defect categories, artificial semantic features are integrated cross-modally with ultrasonic inspection signal features. In this way, a common semantic space containing seen and unseen classes is constructed to achieve the detection of various defects. Meanwhile, to alleviate the problem of extreme imbalance of training data between the seen and unseen classes in ZSL model training, a ZSL model Feature-GAN-ZSL (FGZ) fused with a generative adversarial network (GAN) is proposed. The model utilizes a Feature-GAN network to generate unseen class features during training and adds a classifier to enhance the generation of features with stronger discriminative power. In the experiments, sample data for porosity, incomplete penetration, and cracks were used as visible classes, and samples for incomplete fusion and slag entrapment were used as unseen classes. Five state-of-the-art models in the ZSL domain were compared. The results show that the FGZ model has a good ability to recognize various defects, not only the types of defects that participated in the training but also the defects that did not participate in the training. This plays a perfect role in dealing with various pipeline welding defects.

The Application of Trenchless Piping in Sandbar Drainage Network Renovation Projects

In this study, the research progress of trenchless piping technology for pipes with a large diameter was reviewed. The geological conditions of the sandbar in Xiangyang were taken into account in this study. This paper highlights the construction process management of the pipeline network project in Yuliangzhou Starting Area of Xiangyang City. Research was carried out in the aspects of optimizing mud ratio, controlling pipeline elevations, pipeline welding, and trenchless pipeline construction in limited spaces, stable support during pit excavation, and controlling the spacing between the junctions of two pipe segments. The research resulted in excellent outcomes and ensured safe construction, and the quality requirements were also met.

Implementation of a two-stage algorithm for NG-GMAW seam tracking and oscillation width adaptation in pipeline welding

Seam tracking and oscillation width adaptation could effectively reduce the sidewall fusion defects in automatic narrow-gap gas metal arc welding (NG-GMAW). This study proposes a two-stage algorithm to realise seam tracking and oscillation width adaptation in NG-GMAW pipeline welding. The first stage involves using the principal component analysis (PCA) eigenvectors to adjust the oscillating centre, enabling seam tracking and oscillation width adaptation for the hot and fill layer. In the second stage, dataset was prepared based on the tracking data from the first stage, which can guide the welding torch trajectory for the cap layer. Experimental results show that the proposed scheme achieves seam tracking and oscillation width adaptation with an accuracy of 0.1 mm.

Comparative study of microstructure and toughness of automatic welded joints of X70 pipeline steel with no slope and 25° slope

X70 pipeline steel is the most widely used for oil and gas pipeline transportation. When it is used in mountainous areas, the probability of pipeline failure increases dramatically due to the lack of toughness of welded joints. In order to ensure the welding quality, fully automatic welding has become the trend of pipeline welding. In this paper, X70 pipeline steel is butt welded by fully automatic welding under the condition of both no slope and 25° slope, and the microstructure and the toughness of two welded joints were observed and measured. The results show that the slope affects grain size and impact toughness. The grain size of the coarse-grained heat-affected zone (CGHAZ) in the root weld at the lower groove is larger than that at the upper groove. The impact energy of the heat-affected zone (HAZ) at the upper groove is significantly higher than that at the lower groove. For no slope welded joint, the weld CTOD value in the NQ direction is the lowest. For the 25° slope welded joint, the weld CTOD values are lower than HAZ CTOD values. Meanwhile, the HAZ CTOD values in the NP direction are significantly lower than those in the NQ direction. The fracture morphology of impact and CTOD of two welded joints is similar. The root-weld CGHAZ at the lower groove is the weakest area in the welded joint. For welding under slope conditions, the effect of slope on the performance of welded joints must be taken seriously.

Mathematical Modeling of the Argon Arc Welding Process. Part 2. Welding of HP40NbTi Alloy Pipelines

Mathematical Modeling of the Argon Arc Welding Process. Part 2. Welding of HP40NbTi Alloy Pipelines

In-service welding of X70 steel gas pipeline: Numerical and experimental investigations

The in-service welding process is widely used to repair defective gas pipelines, which allows the damaged section to be repaired without interrupting gas flow. In this way, consumers and gas transportation companies will be able to minimize losses. In this study, a thermomechanical finite element analysis model was developed to examine the effects of pipe pressure, thickness, diameters, and welding heat input on temperature history and residual stress. The results were analyzed using the design of the experiment method (DOE). According to the results, these parameters significantly affect the thermal distribution and pipe yield stress at the welding point by 48.8%, 29.4%, 15.1 and 6.9%, respectively. On the basis of optimal conditions, in-service welding is conducted. Investigations were conducted on the microstructure, temperature distribution, and residual stresses induced during the in-service welding of API 5L X70 steel pipelines. As a result, it was determined that the HAZ microstructure consists primarily of ferrite, bainite, and M/A constituents, whereas the pipe microstructure undergoes fewer phase transformations. In order to validate the numerical model, mechanical tests, as well as hole drilling, were conducted to gather data, and a combination of equivalent carbon and T8/5 was used to determine hardness at various locations in the HAZ in order to determine which areas are susceptible to cold cracking. The numerical results and the experimental results were highly concordant.

Research on residual stress and hydrogen diffusion of X80 pipeline welded joint

Hydrogen enrichment induced by pipeline welding residual stress is a key issue affecting the safe service of hydrogen pipelines. In order to explore the influence of welding residual stress on hydrogen diffusion and the effect of heat treatment, an X80 pipeline model with a six-layer girth weld was established using ABAQUS software, and coupling analysis of temperature field, stress field, and hydrogen diffusion was carried out. The distribution of residual stress in the welding area and local inclusion area was analyzed, the relationship between the hydrostatic stress and hydrogen diffusion of the pipeline, and the influence of the direction and shape of the inclusion on hydrogen diffusion were explored, and the effect of post-weld heat treatment on the residual stress and hydrogen diffusion in the welding area and local inclusion area was studied. The hydrostatic stress curve is similar to the hydrogen concentration distribution curve, and hydrostatic stress can be considered the main driving force for hydrogen diffusion in the pipeline. As the angle between the inclusions and the hydrogen diffusion direction increases, the hydrogen diffusion flux will gradually decrease. The more slender the inclusions, the stronger the diffusion ability of parallel or 45° inclusions, and the weaker the diffusion ability of perpendicular inclusions. Hydrogen enrichment occurs in inclusions or inclusion boundaries before and after post-weld heat treatment of the pipeline, and the internal hydrogen concentration of hard inclusions is higher than that of soft inclusions. Post-weld heat treatment can significantly reduce welding residual stress and hydrostatic stress, thereby reducing hydrogen enrichment concentration.

An automated nondestructive testing system for the surface of pressure pipeline welds

AbstractPressure pipelines are widely used in hydropower generation, oil and gas transmission, and other fields. After years of operation, a pressure pipeline needs regular maintenance to ensure its safety. At present, manual detection methods are unable to meet this demand. An automatic pressure pipeline detection technology is urgently needed to achieve improved efficiency and accuracy. On the basis of the above requirements, a wall‐climbing robot is designed for automatic pressure pipe inspection and maintenance tasks. Moreover, rapid nondestructive testing of welds on the inner surface of pressure pipelines was performed, and a weld tracking function was developed for wall‐climbing robots. We propose an algorithm framework for weld recognition and centerline extraction by combining computer vision technology with traditional image processing technology using visual images. The experimental verification of the wall‐climbing robot designed in this paper and the algorithm framework for weld recognition and centerline extraction were performed based on actual pressure pipelines. The results show that the algorithm framework developed based on the wall‐climbing robot equipped with an industrial camera for pressure pipeline weld detection can achieve greatly improved efficiency, and the actual weld identification accuracy can exceed 90%, which is very meaningful for practical applications.

Method for detecting pipeline spatial attitude using point cloud alignment

This paper introduces a new method for detecting the spatial attitude of pipeline welding based on point cloud registration. The method aligns the measured point cloud of the pipeline with the CAD model point cloud using point cloud registration and guides the pipeline adjustment accordingly. This automated measurement method has resolved the inefficiencies and potential for rework that can arise from manual instrument operation during pipeline pose detection, and has subsequently improved detection efficiency and accuracy. In addition, we propose a coarse registration algorithm based on the axial line of pipeline point clouds to help facilitate the alignment process. We conducted simulation experiments using 3D CAD models, which demonstrated that our coarse registration algorithm outperformed the other three compared methods in terms of accuracy. The simulation experiments also proved that the absolute value of the overall method's relative error was less than 0.1%.

Effect of Chloride and Sulfate Ions on the SCC of API-X70 Pipeline Welds in Diluted Carbonated Solutions

Effect of Chloride and Sulfate Ions on the SCC of API-X70 Pipeline Welds in Diluted Carbonated Solutions

Corrosion resistance and mechanism of X100 pipeline steel laser-metal active gas hybrid welds with Cr containing welding wire in NS4 solution

The corrosion mechanism of pipeline steel laser-metal active gas (MAG) hybrid welds have been widely concerned with its applied in pipeline welding. The corrosion mechanism of X100 pipeline steel laser-MAG hybrid welds with Cr containing welding wire in NS4 solution was investigated. The corrosion products of weld metal (WM) had a double-layer structure, of which the outer and the inner layer were composed of FeOOH and Cr(OH)3, respectively. Compared with the base metal (BM), WM had higher Cr content and more homogeneous microstructure, so it can form dense corrosion layer, making its corrosion resistance reach the level of the BM.

Defect signal intelligent recognition of weld radiographs based on YOLO V5-IMPROVEMENT

Internal pipeline weld defects cause pipeline cracking accidents, whereas X-ray detection can detect these defects. The deep learning-based intelligent defect identification model of weld radiographs extracted weld defects automatically through a convolutional neural network, thereby eliminating the subjective interference of human factors and improving the quality and speed of film evaluation. By proposing the YOLO V5-IMPROVEMENT model and adding the CA attention mechanism, SIOU loss function, and FReLU activation function, this paper improved the ability to detect small targets, capture low-sensitivity spatial information, and perform global optimization. A total of 7500 radiographs containing weld defects of a Chinese oil and gas long-distance pipeline were selected for training, verifying, and testing the model developed in the paper. Precision and recall of the YOLO V5-improvement presented in this paper reached 92.2 % and 92.3 %, which were 10.7 % and 12.5 % higher than YOLO V4, and 9 % and 11.2 % higher than the unimproved YOLO V5 model, respectively. It is confirmed that YOLO V5-IMPROVEMENT has high accuracy and high robustness and that applying this model to the intelligent defect identification of weld ray images can significantly improve detection efficiency and reduce the misjudgment rate.

Exploring friction stir welding for joining 316L steel pipes for industrial applications: Mechanical and metallurgical characterization of the joint and analysis of failure

Efficient welding of pipelines is critical for industries like power plants. Friction stir welding (FSW) has shown the ability to provide higher strength joints and is envisaged to be suitable for joining industrial pipelines as well. Hence, the present study assesses the application of FSW for joining 316L stainless steel industrial grade pipes. The welding is carried out by varying the process parameters, viz., spindle speed (rpm) and welding speed (mm/min), within a suitable range under force control mode. Microhardness and tensile tests have been conducted to evaluate the mechanical strength of the joints, whereas microstructure evaluation by optical microscopy (OM) and field emission scanning electron microscopy (FE-SEM) has been done in order to comprehend the mechanical performance of the weldments. It is found that both the selected welding process parameters (spindle speed and welding speed) have significant influence on the metallurgical and mechanical properties of the welded joints. High grain refinement due to dynamic recrystallization (DRX) in the stir zone results in enhanced hardness. At the optimal combination of parameters, i.e., 400 rpm spindle speed and 50 mm/min welding speed, the weld zone displays higher tensile strength than that of base metal. Also, at the same combination of parameters, stir zone of the welded samples displayed maximum hardness (255HV0.5kg). Some of the weldments displayed early failure during tensile tests. The present study also explains the cause of these failures. Optimum amount of heat generation during FSW process is key to achieving defect free good quality weldments.