Non-destructive tests are a major evaluation process in the metal, oil, and gas industries. In these industries, weld defect inspection is one of the important parts of testing. Manual inspection may ...complicate for proper justification and it produces false identification during detection of weld defects in the human perspective less situations. X-radiography testing is utilized in the weld defect inspection and now this method is mostly outdated further it is enhanced into analysis on X-radiography digital image. Therefore, an autonomous weld defect detection and classification is required for the error free inspection. This paper proposed an autonomous technique for weld defects detection and classification using multi-class support vector machine (MSVM) in X-radiography images where weld defects such as porosity, gas pores, tungsten inclusions, longitudinal cracks, lack of penetration, and slag inclusions are considered. Three modules are involved in this proposed method. In the first module, the images were smoothened using modified anisotropic diffusion method. The segmentation process was performed using improved Otsu’s method in the second module. Finally, the features of the region of interest are extracted and given as input to the multi-class support vector machine with the kernel Gaussian radial basis function. The results of proposed scheme were compared with artificial neural network, Bayes and MSVM with other kernel functions like multi-layer perceptron and polynomial. The implementation and experimental outcomes reveal that the proposed scheme can positively detect and classify the weld defects in X-radiography images.
In this paper, the airtightness problem caused by welding defects in the manufacturing process of composite mold is analyzed. The mechanism analysis of the weld defects of composite mold is ...conducted. According to the mechanism of weld failure, the welding process monitoring, weld metallographic analysis, weld X-ray inspection, panel airtightness detection, and other methods are used to evaluate the welding quality of different welding forms in the welding process of composite mold panels. The measures to optimize the welding quality of composite mold panels are put forward. Finally, through the actual verification, it is proved that the optimized air-tightness welding method is stable and reliable.
•Defect statistics for modern ships is given, which is non-existent in public domain.•It was found that HLAW process has lower defect rates compared to FCAW and SAW.•The probability distributions ...that best represent defect length data was determined.•The welding quality of studied ships was compared to published data from 1980s.
Ships undergo cyclic loading which combined with weld defects can cause fatigue failure. Remaining fatigue life of structures containing defects can be estimated using the defect size. The defect data for ships is non-existent in literature or belong to old offshore structures. In this research, the data collected from two ships are presented. The statistical analysis of the data shows that the Hybrid Laser Welding has lower defect rates than other common arc welding processes indicating that less quality control inspection may be allowed. The defect length values from the studied ships were smaller than those from offshore structures.
The use of multi-materials structures is nowadays one of the most sought solutions to decrease weight and reduce both emission of greenhouse gases and fuel consumption in the automotive industry. ...Dissimilar joining of aluminum (Al) alloys to steels by fusion-based welding technologies is often difficult to achieve as a result of the significant mismatch in these materials’ physical and chemical properties. Moreover, when mixed in the liquid state, hard and brittle intermetallic compounds are easily formed. Due to characteristics that include high processing speed, flexibility and energy density, multiple attempts have been made to join Al to steel using laser-based processes. This thorough review article provides a comprehensive and exhausting analysis of the recent achievements and progress on joining of Al alloys to steel by various laser-based joining processes, including laser keyhole welding, laser welding-brazing, laser-arc welding, laser-assisted friction stir welding, laser roll pressure welding and joining based on laser additive manufacturing. This paper also evaluates the joining conditions, filler materials, phase constitution, microstructure, mechanical properties and joining mechanisms associated to each process. Furthermore, special emphasis is given to factors affecting the joint strength such as welding defects, joint geometry, intermetallic compounds formation and interfacial strength. The review is then concluded with an outlook providing the summary and future trends of this field.
The paper offers a solution to the problem of detecting and recognizing surface defects in welded joints that appear during tungsten inert gas welding of metal edges. This problem belongs to the ...machine vision. Welding of stainless-steel edges is carried out automatically on the pipe production line. Therefore, frames of video sequences are investigated. Images of some welding defects are shown in the paper. An algorithm proposed by the authors is used to detect welding defects in the video sequence frames, the efficiency of which has been confirmed experimentally. The problem solution of welding defects recognition is based on the use of traditional machine learning methods: support vector machine and artificial neural network. To build classification models, a labeled dataset containing automatically extracted texture features from the areas of welding defects detected in the video sequences was created. An analysis was performed to identify the strength of the correlation of texture features between each other and the dependent variable in the dataset for dimensionality reduction of the feature vector. The models were trained and tested on datasets with different numbers of features. The quality of the classification models was evaluated based on the accuracy metric values. The best results were achieved by the classifier built using the support vector machine with a chi-square kernel on a training sample with two features. The build models allow automatic recognition of such welding defects as lack of fusion and metal oxidation. The computational experiments with real video sequences obtained with a digital camera confirmed the possibility of using the proposed solution for recognizing surface welding defects in the process of manufacturing stainless steel pipes.
Class-imbalanced weld defect recognition, which realizes defect recognition via learning features of class-imbalanced X-ray images, is an emerging but challenging task. Nevertheless, the existing ...studies on the class-imbalanced problem mainly focus on large-scale data, and it is difficult to extract high-quality features from the insufficient industrial data, resulting in weak recognition performance. To address the above issue, this article comprehensively learns features from the perspective of basic-class and cross-class, on this basis, a novel hybrid feature learning model for class-imbalanced weld defect recognition is proposed. First, an image acquisition method completed by photographing, scanning, and sampling is designed to collect the class-imbalanced X-ray images. Second, a hybrid feature learning model is proposed to learn the distinctive and effective features from acquired images, so that the class-imbalanced data are mapped to a balanced feature distribution. Third, with the distinguishable features learned by our hybrid feature learning model, an unbiased defect recognition model can be trained to recognize different types of defects. The practical weld data W-PPLN, W-MTL, and W-GDXray are adopted in the experiments, and the experimental results show that our method outperforms the state-of-the-art methods on the task of class-imbalanced weld defect recognition.
Weld defect recognition has become a major research topic as it ensures the security of industrial equipment. Existing methods for this task rely on abundant labeled X-ray images, which however, are ...not always available due to costly annotation and environmental complexity, leading to undesirable recognition performance. To solve the above issues, a dual-graph interactive consistency reasoning network (DGICR-Net) is proposed in this paper. Firstly, a data acquisition approach completed by X-ray radiographic inspection, film scanning and X-ray image annotation is presented, so as to obtain weld X-ray images. Secondly, a dual-graph interactive consistent reasoning network is proposed, where the instance graph and the distribution graph are constructed to capture the global correlation information between multiple X-ray images, and then the two graphs are continuously updated in an interactive and consistent reasoning way, thus the label information can be aggregated from limited labeled X-ray images to unlabeled X-ray images. Finally, three losses containing the instance graph loss, the distribution graph loss and the structure consistency constraint loss are designed to completely train the overall DGICR-Net, enabling achieving weld defect recognition with limited labeled X-ray images. Three experiments are conducted on the datasets PPL, MTL and GDXray, and the results demonstrate that the proposed DGICR-Net can achieve superior recognition performance than other state-of-the-art methods for limited labeled X-ray images.
Abstract
Electro-fusion welding is a common method for high-density polyethylene (HDPE) pipes. It has a high degree of automation and mature technology. However, during the welding process, various ...buried defects are easily generated. In this paper, HDPE pipes with welding defects were processed, and X-ray technology was used to detect the defects. The results show that: The X-ray detection technology is sensitive to the defects of sockets and ultralight clay-filled holes. But it is difficult to distinguish the size of the holes. Unfilled holes are partially fused during the welding process, which makes them difficult to detect. In addition, the defects of cold-welding and unscratched oxide skin cannot be detected.
In situ detection of welding defects: a review Madhvacharyula, Anirudh Sampath; Pavan, Araveeti V Sai; Gorthi, Subrahmanyam ...
Welding in the world,
04/2022, Volume:
66, Issue:
4
Journal Article
Peer reviewed
Weld defect detection is a crucial aspect for improving the productivity and quality of the welding process. Several non-destructive methods exist for the identification of defects post weld ...deposition. However, they only help assess the quality of the component and offer no inputs while the welding process is being performed. Real-time or in situ weld defect detection aids in the detection of defects during the welding process, allowing to take corrective measures or halt the welding to avoid further wastage of time and material. The current paper provides a brief description of various types of weld defects and the commonly used non-destructive testing (NDT) techniques used for identifying weld defects. It then proceeds to provide a detailed review of various methods available for in situ weld defect detection, classifying them based on their input signals. It also classifies the methods based on the type of algorithm used, along with an intuitive explanation of the commonly used algorithms in weld defect detection. The methods covered in this manuscript make use of different input signals that include audio, welding current and voltage, and optical signals also highlighting methods that use a combination of the abovementioned signals for in situ prediction of weld defects. A critical analysis of the efficacy, advantages, and drawbacks of each method is presented. Further, this work highlights a few research gaps identifying avenues for future research in this area.
•A dynamic model for laser welding of aluminum alloy T-joints assisted by solder patch is proposed.•The temperature and flow fields during oscillating laser welding are calculated and analyzed.•The ...degassing condition of molten pool is improved during oscillating laser welding.•The simulation results are kept consistent with experimental results.
During oscillating laser welding of aluminum alloy T-joints assisted by solder patch, it is found that the pore defect can be reduced and the high quality welded T-joints are obtained. To understand the effect of oscillating laser on the formation process of T-joint welds, a dynamic model for laser welding of aluminum alloy T-joints assisted by solder patch is developed in this paper to analyze the dynamic behaviors of keyhole and molten pool. Based on the dynamic model, the morphology characteristics of keyhole and the evolution processes of molten pool temperature and flow fields during conventional laser welding and oscillating laser welding are calculated and compared. The simulation results are found to be kept consistent with the experimental results. It is demonstrated that the keyhole is characterized with better morphology and higher stability and the degassing condition of molten pool is improved during oscillating laser welding compared with conventional laser welding. The molten pool temperature and flow fields are much more uniform during oscillating laser welding. The obtained results are efficient for suppressing weld defects and achieving high quality welding of aluminum alloy T-joints.
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