Journal articles: 'Weld defect'

1

Chen, Y., F. V. Lawrence, C. P. L. Barkan, and J. A. Dantzig. "Weld defect formation in rail thermite welds." Proceedings of the Institution of Mechanical Engineers, Part F: Journal of Rail and Rapid Transit 220, no. 4 (July 2006): 373–84. http://dx.doi.org/10.1243/0954409jrrt44.

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Ishii, Akira, Vakhtang Lachkhia, Yasuo Ochi, and Masayuki Akutsu. "Recognition of Internal Weld Defects by Defect Model." Transactions of the Japan Society of Mechanical Engineers Series A 60, no. 578 (1994): 2440–45. http://dx.doi.org/10.1299/kikaia.60.2440.

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Muhtadan, Risanuri Hidayat, Widyawan, and Fahmi Amhar. "Weld Defect Classification in Radiographic Film Using Statistical Texture and Support Vector Machine." Advanced Materials Research 896 (February 2014): 695–700. http://dx.doi.org/10.4028/www.scientific.net/amr.896.695.

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Weld defect identification requires radiographic operator experience, so the interpretation of weld defect type could potentially bring subjectivity and human error factor. This paper proposes Statistical Texture and Support Vector Machine method for weld defect type classification in radiographic film. Digital image processing technique applied in this paper implements noise reduction using median filter, contrast stretching, and image sharpening using Laplacian filter. Statistical method feature extraction based on image histogram was proposed for describing weld defects texture characteristic of a radiographic film digital image. Multiclass Support Vector Machine (SVM) algorithm was used to perform classification of weld defects type. The result of classification testing shows that the proposed method can classify 83.3% correctly from 60 testing data of weld defects radiographic films.

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Cui, Wei, Hai-yan Xing, Min-zheng Jiang, and Jian-cheng Leng. "Using a New Magnetic Flux Leakage Method to Detect Tank Bottom Weld Defects." Open Petroleum Engineering Journal 10, no. 1 (March 31, 2017): 73–81. http://dx.doi.org/10.2174/1874834101710010073.

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Background: The weld is an important connection part of the tank bottom but during the process of manufacturing and through its use, it frequently produces defects and brings serious hidden danger in the process of safety production. Objective: This paper develops a new magnetic flux leakage testing system for tank bottom weld defects and proposes an extraction method for the weld defect. It can be used in the detection and visual evaluation of the weld defects. Method: A continuous non-contact scanning method is used in the rectangular slot defect in the different regions of the weld by using a new magnetization system that is vertical to the travelling direction. The characteristics of the weld and the defect are transformed into accurate two-dimensional grayscale graphics through grayscale linear transformation. This is done through the combination of histogram equalization, Otsu’s method of binaryzation, morphologically removing small objects, edge detection, and then structuring a morphologically optimized edge extraction algorithm for edge detection on the grayscale. The displayed grayscale outline locates and quantifies the defects. Conclusion: The results indicated that this method can directly indicate the defect shape, location and other information, the visual display of the magnetic flux leakage testing of the weld defects was also realized. It solved difficulties associated with the magnetic flux leakage method being used in the weld testing and showed how weld detection equipment can be used in the detection and visual evaluation of the weld defects.

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Ben Gharsallah, Mohamed, and Ezzeddine Ben Braiek. "Weld Inspection Based on Radiography Image Segmentation with Level Set Active Contour Guided Off-Center Saliency Map." Advances in Materials Science and Engineering 2015 (2015): 1–10. http://dx.doi.org/10.1155/2015/871602.

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Radiography is one of the most used techniques in weld defect inspection. Weld defect detection becomes a complex task when uneven illumination and low contrast characterize radiographic images. In this paper we propose a new active contour based level set method for weld defect detection in radiography images. An off-center saliency map exploited as a feature to represent image pixels is embedded into a region energy minimization function to guide the level set active contour to defects boundaries. The aim behind using salient feature is that a small defect can frequently attract attention of human eyes which permits enhancing defects in low contrasted image. Experiment results on different weld radiographic images with various kinds of defects show robustness and good performance of the proposed approach comparing with other segmentation methods.

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Sudheera, K., N. M. Nandhitha, Lakshmi Mohanachandran, Parithosh Nanekar, B. Venkatraman, and B. Sheela Rani. "DWT Based Automated Weld Pool Detection and Defect Characterisation from Weld Radiographs." Advanced Materials Research 984-985 (July 2014): 573–78. http://dx.doi.org/10.4028/www.scientific.net/amr.984-985.573.

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Industrial Radiography is the most widely accepted NDT technique for weld quality in industries. As it is an indirect method, defect type and nature must be obtained by analyzing the radiographs. Manual interpretation of radiographs is subjective in nature. So the paradigm shifted to automated weld defect detection system. Though considerable research is done in automated weld defect detection, an accurate domain specific technique has not yet been evolved due to noise, artifacts in radiographs, low contrast between the defect region and the background and difficulty in isolating the defect. The proposed work aims at developing an automated weld defect detection system that enhances the contrast between the object and the background and isolates the weld defect. In this work, real time weld radiographs are acquired and contrast enhancement is performed with DWT. Slag and Porosity are isolated and dimensionally characterized.

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Chen, Jian, Zheng Qiang Lei, Fu Xiang Wang, Ting Wang, and Ming Fei Li. "Fitness for Purpose Assessment of Girth Weld Defect Based on In-Line Inspection." Applied Mechanics and Materials 853 (September 2016): 524–28. http://dx.doi.org/10.4028/www.scientific.net/amm.853.524.

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The girth weld defect is one of the most common types of defects on oil and gas pipelines, which can have a strong impact on the operation safety. Several girth weld failure accidents have occurred on PetroChina’s pipelines in recent years. In this paper, PetroChina’s current work on inspection and fitness for purpose assessment of girth weld defects is summarized. The in-line inspection has been proved to be the best practice for oil and gas pipeline defect inspection, but there are still some technical issues such as defect characterization and parameter selection. Fitness for purpose assessment methods for girth weld defects include strength assessment method based on plastic collapse, FAD method based on both plastic collapse and fracture, simplified factor method and numerical analysis method based on finite element. It is critical to identify the actual type of defects detected in in-line inspection to select an appropriate assessment method. The identification of various loads and the selection of appropriate material parameters are also important issues in assessment procedure. The key techniques to be developed include defect characterization, load identification, assessment of defects on high grade steel pipes, reliability-based assessment, etc.

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The Welding Institute. "Weld defect specimens for training." NDT International 23, no. 2 (April 1990): 123. http://dx.doi.org/10.1016/0308-9126(90)91986-4.

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The Welding Institute. "Weld defect specimens for training." NDT & E International 23, no. 2 (April 1990): 123. http://dx.doi.org/10.1016/0963-8695(90)91097-8.

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10

Ye, Jing, Guisuo Xia, Fang Liu, Ping Fu, and Qiangqiang Cheng. "Weld defect inspection based on machine vision and weak magnetic technology." Insight - Non-Destructive Testing and Condition Monitoring 63, no. 9 (September 1, 2021): 547–53. http://dx.doi.org/10.1784/insi.2021.63.9.547.

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This study proposes a weld defect inspection method based on a combination of machine vision and weak magnetic technology to inspect the quality of weld formation comprehensively. In accordance with the principle of laser triangulation, surface information about the weldment is obtained, the weld area is extracted using mutation characteristics of the weld edge and an algorithm for identifying defects with abnormal average height in the weld surface is proposed. Subsequently, a welding seam inspection process is developed and implemented, which is composed of a camera, a structured light sensor, a magnetic sensor and a motion control system. Inspection results from an austenitic stainless steel weldment show that the method combining machine vision and magnetism can identify defect locations accurately. Comprehensive analysis of the test results can effectively classify surface and internal defects, estimate the equivalent sizes of defects and evaluate the quality of weld formation in multiple dimensions.

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Hou, Wenhui, Dashan Zhang, Ye Wei, Jie Guo, and Xiaolong Zhang. "Review on Computer Aided Weld Defect Detection from Radiography Images." Applied Sciences 10, no. 5 (March 10, 2020): 1878. http://dx.doi.org/10.3390/app10051878.

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The weld defects inspection from radiography films is critical for assuring the serviceability and safety of weld joints. The various limitations of human interpretation made the development of innovative computer-aided techniques for automatic detection from radiography images an interest point of recent studies. The studies of automatic defect inspection are synthetically concluded from three aspects: pre-processing, defect segmentation and defect classification. The achievement and limitations of traditional defect classification method based on the feature extraction, selection and classifier are summarized. Then the applications of novel models based on learning(especially deep learning) were introduced. Finally, the achievement of automation methods were discussed and the challenges of current technology are presented for future research for both weld quality management and computer science researchers.

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Shen, Xiao Qin, Fu Sheng Yu, and Ying Bao. "Eddy Current Detection for Weld Defect and Automatic Separating System for Defective HFW Pipe." Applied Mechanics and Materials 457-458 (October 2013): 344–49. http://dx.doi.org/10.4028/www.scientific.net/amm.457-458.344.

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An eddy current detection and automatic separating system is developed to defect the weld defects in HFW pipe and separate the defective pipe. Application of phase sensitive detector, the pure signal of the weld defect is obtained through a low-pass filter. The information of the weld defect signal, the displacement of flying saw and the welding speed of welded pipe are fused together. The mathematical model is established for unfixed length truncation of the defective welded pipe. An automatic separating system is designed to cut and separate the defective welded pipes in the production line. The practice shows that this system can accurately detect the weld defect, cut the welded pipe in the perfect position and maximumly reserve the welded pipe without defect. This detection and separating system can improve the quality of the welded pipes and avoid the waste.

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Gheisari, Yousof, Hamed Pashazadeh, Jamal Teimournezhad, and Abolfazl Masoumi. "Weld defect formation in FSWed coppers." Journal of Materials Engineering and Performance 23, no. 6 (April 29, 2014): 2000–2006. http://dx.doi.org/10.1007/s11665-014-0888-9.

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14

Shiwa, M., A. Yamaguchi, M. Sato, S. Murao, and M. Nagai. "Acoustic Emission Waveform Analysis From Weld Defects in Steel Ring Samples." Journal of Pressure Vessel Technology 121, no. 1 (February 1, 1999): 77–83. http://dx.doi.org/10.1115/1.2883671.

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Acoustic emission (AE) signals from weld defects, incomplete penetration (IP), slag inclusion (SI), and porosity (PR), in longitudinal seam welding of UOE steel pipes were evaluated by using an envelope analysis system and waveform analysis system. In test results, the location accuracy of the envelope and the waveform systems during the loading tests were a few 10 mm and a few mm, respectively. AE activity and intensity and waveform type could identify the welding defect types. Three types of AE spherical radiation patterns (AERPs), a monopole mode, a tensile fracture of dipole mode, and a tensile and shear mixed of dipole mode, were observed during the test. Information from cross section observation of the IP by SEM and AERP suggested that activity and intensity of AE signals from welding defects could depend on both stress concentration of the defect and brittleness of the microstructure around the defect.

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Lai, Xin Min, Xin Zhao, Yan Song Zhang, and Guan Long Chen. "Ultrasonic Fast-Identification Expert System of the Auto Stick-Weld Defect Based on Echo-Characteristics Analyzing." Key Engineering Materials 353-358 (September 2007): 2297–300. http://dx.doi.org/10.4028/www.scientific.net/kem.353-358.2297.

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The current inspection methods of spot-weld quality are difficult to achieve an ideal result especially for the stick-weld defect which is one of the most important types of spot-weld defect in the automotive body. This paper thus developed one fast-identification method to identify the joint defects quickly and efficiently based on quantitative analyzing the echo characteristics of ultrasonic curves that could reflect the spot-welding joint defects. The echo-characteristic parameters were analyzed quantitatively through the decision-making bintree of defects and ART networks. After knowledge acquiring and information processing, a fast-identification expert system (FIES) was developed to identify the automotive body spot-weld defects, through collecting the standard ultrasonic curves and quantitatively analyzing. Finally, a series of experiments were conducted to verify the proposed methods and the results showed that FIES is credible and the identification rate can exceed 95% in total test samples.

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Li, Yan, Miao Hu, and Taiyong Wang. "Weld Image Recognition Algorithm Based on Deep Learning." International Journal of Pattern Recognition and Artificial Intelligence 34, no. 08 (November 7, 2019): 2052004. http://dx.doi.org/10.1142/s0218001420520047.

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As an important part of metal processing, welding is widely used in industrial manufacturing activities, and its application scenarios are very extensive. Due to technical limitations, the welding process always unavoidably leaves weld defects. Weld defects are extremely hazardous, and the work used must be guaranteed to be defect-free, regardless of the field. However, manual weld inspection has subjective factors such as inefficiency and easy missed detection, and although some automatic weld inspection methods have appeared, these traditional methods still do not meet actual demand in terms of detection time and detection accuracy. Therefore, there is a need for a higher quality weld image automatic detection method to replace the manual method and the traditional automatic detection method. In view of the above, this paper proposes a weld seam image recognition algorithm based on deep learning. The Adam adaptive moment estimation algorithm is chosen as the backpropagation optimization algorithm to accelerate the training of convolutional neural networks and design an independent adaptive learning rate. Through the simulation of the collected 4500 tube images, the adaptive threshold-based method is used for weld seam extraction. The algorithm proposed in this paper is compared with the weld seam recognition method based on image texture feature value distribution (ITFVD) and the SUSAN-based weld defect target detection method. The results show that the proposed method can identify weld defects in a short time on different sizes of weld images, and can further detect the type of weld defects. In addition, the method in this paper is better than the other two methods in the false detection rate, recall rate and overall recognition accuracy, which shows that the experimental results have achieved the expected results.

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Stephen, Distun, and Dr Lalu P.P. "Development of Radiographic Image Classification System for Weld Defect Identification using Deep Learning Technique." International Journal of Scientific & Engineering Research 12, no. 5 (May 25, 2021): 390–94. http://dx.doi.org/10.14299/ijser.2021.05.01.

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Weld defect identification from radiographic images is a crucial task in the industry which requires trained human experts and enough specialists for performing timely inspections. This paper proposes a deep learning based approach to identify different weld defects automatically from radiographic images. To employ this a dataset containing 200 radiographic images labelled for four types of welding defect- gas pore, cluster porosity, crack and tungsten inclusion is developed. Then a Convolutional Neural Network model is designed and trained using this database.

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Xu, Ge Ning, and Kai Hao. "Research on Weld Defects Simulation and Performance Assessment of Solid-Web Type Telescopic Jib." Applied Mechanics and Materials 687-691 (November 2014): 236–42. http://dx.doi.org/10.4028/www.scientific.net/amm.687-691.236.

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To research the failure mode of solid-web type telescopic jib typical weld, extract and quantify failure characteristics, obtain failure regularity of jib performance and solve the problem that welds do not be established by conventional finite element analysis. The weld strength is replaced by the maximum stress of the corresponding section, which may cause the simulation results low and distortion, and cannot simulate weld defects. A new modeling method is used by this article, which establishes and simplifies the weld physical shape, simulate and analyze weld strength. The Life and Death element technology which is based on the ANSYS software is used to simulate weld defects. The weld strength that is obtained by conventional analysis simulation is 110MPa, after processing, stress of weld model without defects is 395MPa, the theoretical value be calculated is 437MPa, stress of weld model with defect 1, 2, 3 respectively is 586MPa, 402MPa and 475MPa. Through comparing the simulation value and the theoretical value and the comparison between the simulation values, the simplified model that is proved is reasonable, the technical route which is used to simulate weld defect by Life and death element technology is feasible. It can be used to predict failure critical value under typical working condition, assess whether the weld failure. The working condition of jib can be simulated, and the main factors can be retrospect.

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Xiao, Kai, Ya Kun Shi, Qing Zeng Ma, Jun Zhang, and Xiao Hong Li. "The Intelligent Ultrasonic System for Quality Testing of Weld Connections in Turbine Runners." Advanced Materials Research 774-776 (September 2013): 1543–46. http://dx.doi.org/10.4028/www.scientific.net/amr.774-776.1543.

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The intelligent ultrasonic testing system possessing capabilities of defect tracking and defect simulated analyzing has been developed in this paper according to the characteristic of defects existing in weld connections in turbine runners. The passage then introduces the testing system from angles of structure, function and operation, respectively. Finally, the testing system is applied to an in-service turbine runner and the results indicate that desired efficiency and accuracy of detection in the testing system can be achieved and the mean error of the length for defects can be held within 5mm.

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Zhang, Peng Lin, Zheng Bin Wu, Xian Ming Niu, and Zhi Qiang Zhao. "Computer Extraction and Recognition Method Research on Weld Defect." Advanced Materials Research 739 (August 2013): 210–13. http://dx.doi.org/10.4028/www.scientific.net/amr.739.210.

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This paper carry out a kind of defect extraction method .Aiming at the weld image defect extraction accuracy is not high and defect feature selection is undeserved, thus affecting defect recognition rate is not high lead to falsely accused of miscarriage of justice on this condition.Based on image preprocessing to remove noise and strengthen the image, and then the image analysis so as to extract defect finally take defect marking defect feature parameter selection, in order to accurately identify defect.

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Dai, L. S., Q. S. Feng, X. Q. Xiang, J. Sutherland, T. Wang, D. P. Wang, and Z. J. Wang. "Application of USCCD on Girth Weld Defect Detection of Oil Pipelines." Applied Sciences 10, no. 8 (April 15, 2020): 2736. http://dx.doi.org/10.3390/app10082736.

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Globally, more and more attention has been paid to the integrity of Girth Welds (GW) of oil and gas pipelines due to their failures with high consequences. A primary concern is that defects originate during field construction but over time may be subject to external loads due to earth movement. GW defects in newly built pipelines are also assumed to exist but would be much smaller in size, and more difficult to detect, which motivated the investigation into minimum defect detection capabilities of the inspection technologies. This study presents the evaluation results of UltraScan™ Circumferential Crack-Like Detection (USCCD) technology for oil pipeline GW inspection, based upon the pull test and in field data from Inline Inspection (ILI) of pipeline by PetroChina Pipeline Company (PPC) using GE PII (General Electric Company, Pipeline Integrity Inspection) 32” UltraScan™ CCD Tool. The performance of USCCD is given according to the ILI data, pull test results and dig NDE (Non-Destructive Examination). It can be concluded that crack-like defects with clear edges can be detected during ultrasonic propagation; however, the irregular shape of weld makes the inspection more difficult. It is still a challenge to identify the type of defects, and depth sizing can only be classified not quantified, which would require more excavations. However, this technology is feasible for the alternative technology of GW defect inspection.

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De Waele, Wim. "The Interaction of Weld Defects under Plastic Collapse." Materials Science Forum 475-479 (January 2005): 2735–38. http://dx.doi.org/10.4028/www.scientific.net/msf.475-479.2735.

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Multiple defects in welds, when detected, have to be assessed for interaction. Current code rules are based on linear elastic fracture mechanics whereas the failure mode for welds in thin structures is primarily plastic collapse. Results of large-scale tests illustrate that current interaction rules have a high degree of conservatism for plastic collapse conditions. Guidance for the assessment of defect interaction under plastic collapse is proposed.

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Zahran, O., H. Kasban, M. El-Kordy, and F. E. Abd El-Samie. "Automatic weld defect identification from radiographic images." NDT & E International 57 (July 2013): 26–35. http://dx.doi.org/10.1016/j.ndteint.2012.11.005.

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Yazid, Haniza, Hamzah Arof, Hafizal Yazid, and Norazian Abd Razak. "Weld Detect Identification Using Texture Features and Dynamic Time Warping." Applied Mechanics and Materials 752-753 (April 2015): 1045–50. http://dx.doi.org/10.4028/www.scientific.net/amm.752-753.1045.

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In this paper, a simple yet robust algorithm for texture identification using 1 Dimensional Discrete Fourier Transform (1-D DFT) and Dynamic Time Warping (DTW) is presented with illumination variations. In the first stage, several image processing techniques namely Fuzzy C means (FCM) clustering, edge detection, Otsu thresholding and inverse surface thresholding method are utilized to locate the region of interest (ROI) where defects might exist. Next, the image undergoes the feature extraction process using 1-D DFT and finally, the features are classified using DTW. Several defect images consist of 2 types of defect namely the porosity and crack are experimented and classified using the DTW.

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Yao, Deng Zun, Zhi Wen Li, Jian Wu Liu, and Lin Chen. "Application of State of the Art Assessment for Pipeline Girth Weld." Materials Science Forum 898 (June 2017): 1063–68. http://dx.doi.org/10.4028/www.scientific.net/msf.898.1063.

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In the pipeline construction, the girth welds tend to be the weakness because of defects and microstructural heterogeneities. The importance of suitable assessment of various defects in the weld is not only to prevent the cracks from unstable growth to cause catastrophic accident but also can effectively reduce the weld repair to reduce construction cost. Although many welding defects assessment methods and codes have been applied in this field, there are many differences among them. In this paper, the application of weld defect assessment methods was extensively studied. The key points of ECA applications, such as the pipeline axial stress and toughness, have been introduced. Furthermore, some suggestions were given on the application of girth weld ECA assessment.

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Matic, P., and M. I. Jolles. "The Influence of Small Defects on Tensile Specimen Ductility and Symmetry of Deformation." Journal of Engineering Materials and Technology 110, no. 3 (July 1, 1988): 224–33. http://dx.doi.org/10.1115/1.3226041.

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The quantitative translation of physical weld quality into structural integrity prediction depends on accurate characterization of weld material behavior in the presence of fabrication defects. The presence of such defects will, however, significantly influence the response of common material test specimens. If the influence of such defects is fully understood, test specimen data may be interpreted in a more meaningful way. The role of a physically relevant geometric imperfection, in the form of a spherical void defect, on cylindrical tensile specimen response is computationally simulated for HY-100 weld metal. Defect radius and location along the specimen axis are treated as independent parameters. Asymmetry of specimen deformation (in terms of specimen neck location) and specimen ductility (in terms of the reduction of area at failure) are computationally predicted. Results suggest that the neck location does not necessarily coincide with the defect location. Therefore, geometric defects are a sufficient condition for asymmetry of neck location but not a necessary condition for neck formation. In addition, coincidence of the defect and the neck reduces the specimen ductility at failure to a minimum value which depends on defect size. When the defect and neck are separated, the defect free specimen ductility at failure, i.e., the maximum ductility value, is recovered as an upper bound. The transition between these two ductility values is abrupt, despite the continuous nature of the physical problem. Preliminary implications of these results on the assessment of defect criticality are discussed.

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Zhang, Tian Hui, Wen Min Liu, Ren Ping Xu, and Bin Xu. "Effect of Welding Method on Weld Defects of ADB610 Steel." Advanced Materials Research 97-101 (March 2010): 818–21. http://dx.doi.org/10.4028/www.scientific.net/amr.97-101.818.

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Statistical analysis was carried on weld defects of low carbon bainite ADB610 steel using shielded metal arc welding (SMAW) and mixed active-gas arc welding (MAG). By Pareto diagram analysis, although the ratio of porosity air hole using SMAW is slightly higher than the one using MAG, there is no qualitative difference in ADB610 steel weld defect types between two welding methods. And the crack occurs seldom, which indicates ADB610 steel has lower crack-sensitivity using SMAW and MAG. By histogram analysis and rank test, it can be concluded that there is distinctive difference in defect size between SMAW and MAG, and the average size using SMAW is bigger than the one using MAG. So if possible, MAG is recommended for low carbon bainite ADB610 steel.

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Abdul Halim, Suhaila, Arsmah Ibrahim, and Yupiter Harangan Prasada Manurung. "Digital Radiographic Image Enhancement for Weld Defect Detection using Smoothing and Morphological Transformations." Scientific Research Journal 9, no. 1 (June 30, 2012): 15. http://dx.doi.org/10.24191/srj.v9i1.5053.

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Accurate inspection ofweldedmaterials is important in relation to achieve acceptable standards. Radiography, a non-destructive test method, is commonly used to evaluate the internal condition ofa material with respect to defect detection. Thepresence ofnoise in low resolution ofradiographic images significantly complicates analysis; thereforeattaining higher quality radiographic images makes defect detection more readily achievable. This paper presents a study pertaining to the quality enhancement of radiographic images with respect to different types of defects. A series of digital radiographic weld flaw images were smoothed using multiple smoothing techniques to remove inherent noise followed by top and bottom hat morphological transformations. Image quality was evaluated quantitatively with respect to SNR, PSNR andMAE. The results indicate that smoothing enhances the quality ofradiographic images, thereby promoting defect detection with the respect to original radiographic images.

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Deng, Honggui, Yu Cheng, Yuxin Feng, and Junjiang Xiang. "Industrial Laser Welding Defect Detection and Image Defect Recognition Based on Deep Learning Model Developed." Symmetry 13, no. 9 (September 18, 2021): 1731. http://dx.doi.org/10.3390/sym13091731.

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Aiming at the problem of the poor robustness of existing methods to deal with diverse industrial weld image data, we collected a series of asymmetric laser weld images in the largest laser equipment workshop in Asia, and studied these data based on an industrial image processing algorithm and deep learning algorithm. The median filter was used to remove the noises in weld images. The image enhancement technique was adopted to increase the image contrast in different areas. The deep convolutional neural network (CNN) was employed for feature extraction; the activation function and the adaptive pooling approach were improved. Transfer Learning (TL) was introduced for defect detection and image classification on the dataset. Finally, a deep learning-based model was constructed for weld defect detection and image recognition. Specific instance datasets verified the model’s performance. The results demonstrate that this model can accurately identify weld defects and eliminate the complexity of manually extracting features, reaching a recognition accuracy of 98.75%. Hence, the reliability and automation of detection and recognition are improved significantly. The research results can provide a theoretical and practical reference for the defect detection of sheet metal laser welding and the development of the industrial laser manufacturing industry.

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Guo, Wen Ming, and Yan Qin Chen. "An Effective Method for Defects Detection in Radiographic Images of Welds Based on Edge Detection and Morphology." Applied Mechanics and Materials 290 (February 2013): 71–77. http://dx.doi.org/10.4028/www.scientific.net/amm.290.71.

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In the current industrial production, as steel weld X-ray images are low contrasted and noisy, the efficiency and precision can’t be both ensured. This paper has studied three different edge detection algorithms and found the most suitable one to detect weld defects. Combined with this edge detection algorithm, we proposed a new weld defects detection method. This method uses defect features to find the defects in edge images with morphological processing. Compared to the traditional methods, the method has ensured detection quality of weld defects detection.

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Liu, Zhiping, Xingle Liu, Lei Jiang, Ge Lu, and Huilong Liu. "Study on the Heat Transfer Characteristics Performed in the Infrared Thermography Detection of Welded Structure." Open Mechanical Engineering Journal 9, no. 1 (April 17, 2015): 251–59. http://dx.doi.org/10.2174/1874155x01509010251.

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During the weld defect detection, the heat transfer characteristics are closely related to the material properties of welded structure. Based on the electromagnetic induction infrared thermography technology, the heat transfer Characteristics of the welded material are studied with the changing temperature. By using the finite element analysis software COMSOL, the eddy current density and temperature distributions and the law of thermal diffusion were analyzed which provide a reference for the study of heat transfer characteristics of weld defects. The internal temperature dynamic changes of the weld with surface crack or near-surface crack were also discussed. The appropriate time to observe and the key defect location on the steel obtained from the heat conduction process can be applied to the development of heat transfer characteristic analysis for steel weld and weld defects detection.

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Abdul Halim, Suhaila, Arsmah Ibrahim, and Yupiter Harangan Prasada Manurung. "Digital Radiographic Image Enhancement for Weld Defect Detection using Smoothing and Morphological Transformations." Scientific Research Journal 9, no. 1 (June 1, 2012): 15. http://dx.doi.org/10.24191/srj.v9i1.9412.

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Accurate inspection of welded materials is important in relation to achieve acceptable standards. Radiography, a non-destructive test method, is commonly used to evaluate the internal condition of a material with respect to defect detection. The presence of noise in low resolution of radiographic images significantly complicates analysis; therefore attaining higher quality radiographic images makes defect detection more readily achievable. This paper presents a study pertaining to the quality enhancement of radiographic images with respect to different types of defects. A series of digital radiographic weld flaw images were smoothed using multiple smoothing techniques to remove inherent noise followed by top and bottom hat morphological transformations. Image quality was evaluated quantitatively with respect to SNR, PSNR and MAE. The results indicate that smoothing enhances the quality of radiographic images, thereby promoting defect detection with the respect to original radiographic images.

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Liu, Qi, Yu Lan Wei, Bing Li, Meng Dan Jin, and Ying Ying Fan. "Detection Devices and Technologies on Large-Scale Pipe Weld Surface Defect." Advanced Materials Research 580 (October 2012): 445–48. http://dx.doi.org/10.4028/www.scientific.net/amr.580.445.

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The kind and extent of defect can be identified through image processing. First, the weld defect detection device should be constructed, and then the defect imaged should be obtained through rational way, in order to enhance the image quality, image filter and image enhancement method should be use. To ensure the real-time system, the weld region need to segment from the image. After that, the needed defect features need to determine and extract. Finally, the kind, the location and the size of the defect can be defined.

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Venkatraman, Dr B. "Weld Defect Detection Using Iterative Image Reconstruction Methods." Indian Journal of Science and Technology 6, no. 4 (April 20, 2013): 1–6. http://dx.doi.org/10.17485/ijst/2013/v6i4.15.

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Saber, Sara, and Gamal I. Selim. "Higher-Order Statistics for Automatic Weld Defect Detection." Journal of Software Engineering and Applications 06, no. 05 (2013): 251–58. http://dx.doi.org/10.4236/jsea.2013.65031.

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Esther Florence, S., R. Vimal Samsingh, and Vimaleswar Babureddy. "Artificial intelligence based defect classification for weld joints." IOP Conference Series: Materials Science and Engineering 402 (September 20, 2018): 012159. http://dx.doi.org/10.1088/1757-899x/402/1/012159.

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SHIRAISHI, Daijiroh, Takayuki INOUE, Satoru YAMAMOTO, Ryousuke KATOH, Haruo ENDOH, and Tsutomu HOSHIMIYA. "703 Imaging of Weld Defect by Photoacoustic Method." Proceedings of Ibaraki District Conference 2008 (2008): 165–66. http://dx.doi.org/10.1299/jsmeibaraki.2008.165.

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Dang, Changying, Jiansu Li, Wenhua Du, Zhiqiang Zeng, and Rijun Wang. "A novel extraction method for weld defect segmentation seeds using ANDM and clustering." Insight - Non-Destructive Testing and Condition Monitoring 61, no. 12 (December 1, 2019): 706–13. http://dx.doi.org/10.1784/insi.2019.61.12.706.

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To improve the accuracy and reliability in extracting defect segmentation seeds from a weld radiographic testing (RT) image, a novel extraction method (NESS) using clustering and a novel defect detection method (ANDM) that was presented in a previous paper by one of the authors is proposed in this paper. In the proposed NESS, firstly each column of the weld RT image is accurately analysed by ANDM to judge whether or not it really passes through weld defect regions. Most importantly, one or more defect seeds can be acquired if it passes through a defect region. Secondly, all the defect seeds (a defect seed group) of the RT image are extracted by analysing the entire image. Finally, a sorting-based clustering method is proposed to quickly and accurately search for defect segmentation seeds among all the defect seeds, which can solve the problems concerning the difficulty in determining defect segmentation seeds and the heavy calculational burden of defect segmentation. In order to evaluate the performance of the proposed NESS, some clustering and segmentation experiments have been performed. The experimental results reveal that the proposed NESS achieves high accuracy and reliability in extracting defect segmentation seeds from RT images and is helpful in defect segmentation.

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Suyadi, Suyadi. "PENGARUH CACAT LAS PADA SAMBUNGAN PIPA BAWAH LAUT (GIRTH WELD) DENGAN MENGAPLIKASIKAN FAILURE ANALYSIS DIAGRAM (FAD)." Gema Teknologi 16, no. 1 (October 24, 2010): 57. http://dx.doi.org/10.14710/gt.v16i1.367.

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Suyadi, An under sea pipe connection (girth weld) is an area that possibly will get defect. This weld defects have a potential for causing tension concentration, so that there will be preliminary crack. This crack will spread due to cyclic loads received by the structure. This final assignment research is performed for evaluating mechanical integrity and girth weld having weld defect using method of Failure Analysis Diagram (FAD) and it takes a sample of case for South Sumatra West Java pipe belongs to PGN Co.Ltd. FAD is a method, which is good enough for evaluating the integrity of pipe structure having crack like flaw upon the weld. FAD divides two areas, namely safe area and unsafe area constructed by two axis, namely x axis that is stress ratio (Lr) and y axis that is stress intensity ratio (Kr). Stress ratio (Lr) is a ratio between a ref and a yield, mean while stress intensity ratio (Kr) is a ratio (stress intensity factor) between Kl and KIC (fracture toughness material). ANSYS 8.0 software is used for modeling by considering that the defect is in the form of semi elliptic with a/2c variation, that is 0,0469; 0,0938; 0,01406; and 0,1750. The loading upon the pipe is considered as the minimum loading (pull = 8284.25 psi), medium (pull = 41421.25 psi) and maximum (pull = 82842.5 psi). From the analysis result, it is obtained that crack dimension with a/2c 0.175 is not safe upon minimum and medium loading, mean while upon the maximum loading condition, all modeled cracks dimensions cause the pipe is not safe. Keywords : pipeline, girth weld, semi elliptical defect, FAD.

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Zhang, Xiaolong, and Zhenying Xu. "Dispersion of an SH-Guided Wave in Weld Seam Based on Peridynamics Theory." Mathematical Problems in Engineering 2020 (January 31, 2020): 1–9. http://dx.doi.org/10.1155/2020/4802930.

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The dispersion characteristics of shear horizontal- (SH-) guided waves in a weld seam are critical to identifying defects. By considering the force on the virtual boundary layer near the weld surface, a dispersion equation for the SH-guided wave in the weld seam was established here based on the peridynamics method. The wave dispersion equation is similar to the traditional theory. The SH wave in the infinite peridynamics medium has dispersion characteristics, and the group velocity of the SH-guided wave in the weld seam is slightly slower than that in the conventional theory. In the welded structure, the group velocity of the SH-guided wave is unevenly distributed in different regions due to the differences in material parameters between the weld seam and the steel plate and residual weld height on the weld seam. The distance from the different sensors to the defect can be precisely calculated via the group velocity distribution; thus, the defect can be accurately located. By compared with the finite element method and experiments under the same conditions, the reliability of the peridynamics method is verified. We used the group velocity of the SH-guided wave in the weld seam and peridynamics theory to better reflect the experimental conditions versus finite element simulations.

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Ovchinnikov, Viktor, Artem Opalnitskiy, and Aleksandr Drits. "Technological quality support of weld seam obtained by friction stir method." Science intensive technologies in mechanical engineering 2019, no. 12 (December 16, 2019): 11–21. http://dx.doi.org/10.30987/2223-4608-2019-2019-12-11-21.

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Basic defects arising during the friction stir welding (FSW) of aluminum alloy butt-joints are systemized. There are revealed basic reasons of such defects arising such as faulty fusions, a flash, a metal overheating on the right side of a seam. A range of optimum correlations expressing a length of linear displacement of a tool along a joint during its one revolution (feed for one revolution) is defined, in which a qualitative formation of aluminum alloy seams is ensured. A possibility of defect correction by means of the repeated FSW pass is shown.

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Liu, Zhi Hao, and Chao Lu. "Ultrasonic Phased Array 3D Imaging for the Steel Butt Weld Defects." Applied Mechanics and Materials 727-728 (January 2015): 799–803. http://dx.doi.org/10.4028/www.scientific.net/amm.727-728.799.

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Ultrasonic phased array imaging detection technology combinating the focused beam and array probe movement can get powerful test information. It has been widely used in the steel butt weld detection. For making up the limitations of 2D view, in this paper,we used one-dimensional linear array probe, got 2D slice view data obtained by phased array ultrasonic S-scan, through software programming algorithm to realize 3D reconstruction of steel butt weld typical defects. Experiment shows that it can display more intuitive performance of the defects in space. Revealing a better shape, size and orientation information. Providing a reference for the final evaluation of the defect.

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Bhanu Sankara Rao, K., M. Valsan, R. Sandhya, S. L. Mannan, and P. Rodriguez. "Influence of Weld Discontinuities on Strain Controlled Fatigue Behavior of 308 Stainless Steel Weld Metal." Journal of Engineering Materials and Technology 116, no. 2 (April 1, 1994): 193–99. http://dx.doi.org/10.1115/1.2904273.

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Detailed investigations have been performed for assessing the importance of weld discontinuities in strain controlled low cycle fatigue (LCF) behavior of 308 stainless steel (SS) welds. The LCF behavior of 308 SS welds containing defects was compared with that of type 304 SS base material and 308 SS sound weld metal. Weld pads were prepared by shielded metal arc welding process. Porosity and slag inclusions were introduced deliberately into the weld metal by grossly exaggerating the conditions normally causing such defects. Total axial strain controlled LCF tests have been conducted in air at 823 K on type 304 SS base and 308 SS sound weld metal employing strain amplitudes in the range from ±0.25 to ±0.8 percent. A single strain amplitude of ±0.25 percent was used for all the tests conducted on weld samples containing defects. The results indicated that the base material undergoes cyclic hardening whereas sound and defective welds experience cyclic softening. Base metal showed higher fatigue life than sound weld metal at all strain amplitudes. The presence of porosity and slag inclusions in the weld metal led to significant reduction in life. Porosity on the specimen surface has been found to be particularly harmful and caused a reduction in life by a factor of seven relative to sound weld metal. Defect combination of porosity and slag inclusions was found to be more deleterious than the case when either the slag inclusions or porosity was present alone. Discontinuties acted as crack initiation sites and also enhanced crack propagation. The LCF properties of weld samples containing discontinuities have been correlated with the damage and fracture behavior.

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Widyawati, Fauzi, and Lino Marano. "IDENTIFIKASI CACAT LASAN FCAW PADA FONDASI MESIN KAPAL MENGGUNAKAN METODE ULTRASONIC TESTING." Jurnal TAMBORA 5, no. 2 (July 21, 2021): 53–58. http://dx.doi.org/10.36761/jt.v5i2.1124.

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Ultrasonic testing is one of the non-destructive inspection methods for welding results. The ultrasonic testing method has several advantages, namely it can be used to analyze the position of the defect in the object, both the depth of the defect and the dimensions of the defect, and it is an environmentally friendly method. Physical defects that are in solid objects of course cannot be known from direct vision so it is necessary to carry out an inspection of an object to see whether or not there are defects that occur in solid objects. Ultrasonic testing of the results of FCAW welding on the foundation of the ship's engine. FCAW welding is applied to the foundation with two types of welding positions, namely the overhead position coded P1 and the horizontal position coded P2. The test was carried out using a wave frequency of 4 MHz and using a 0° probe for analysis of defects in the area around the weld metal and a 70° probe for analysis of the weld metal. The tests were carried out using the ASME section V and ASTM E164 standards as the standard for determining defects. The test results at the P1 welding position found two types of defects, namely incomplete fusion defects with five welding points with the longest defect length of 40mm and porosity defects with two points with the longest defect length of 30mm. While the results of ultrasonic testing at the P2 welding position found two types of defects, namely slag inclusion defects with a defect length of 35mm and incomplete penetration defects with a defect length of 20 mm. The conclusion of ultrasonic testing is that the difference in welding positions is that the welding position greatly affects the quality of the welding results. The defects resulting from the welding position also vary.

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Li, Yan, Miao Hu, and Taiyong Wang. "Visual inspection of weld surface quality." Journal of Intelligent & Fuzzy Systems 39, no. 4 (October 21, 2020): 5075–84. http://dx.doi.org/10.3233/jifs-179993.

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Welding is an important method for modern material processing. In actual processing, due to the influence of processing accuracy and welding thermal deformation, various defects often appear in the appearance of the weld. At present, visual inspection is mainly used for the appearance inspection of welds. The detection of weld defects mainly depends on the work experience of the staff. Based on the above background, the purpose of this article is to visually inspect the weld surface quality. This article uses visually obtained fringe images of weld contours as information sources to explore a visual-based weld appearance detection algorithm, including the measurement of weld formation dimensions and the detection of weld appearance defects. This algorithm overcomes manual measurements of the misjudgments and omissions caused by eye fatigue and experience differences. It improves the efficiency and accuracy of welding appearance inspection, and meets the needs of automation and intelligence of the entire welding process. In this paper, a subpixel stripe centerline extraction algorithm based on the combination of the Hessian matrix method and the center of gravity method is used; to further improve the accuracy of the extraction of the centerline of the weld seam, this article also performs the work of removing the wrong points and the compensation of the broken seam. Obtain a fringe centerline with better connectivity. Comparing the extraction algorithms of each centerline, the centerline obtained by this method has high accuracy, less time-consuming and high stability. It laid the foundation for the subsequent inspection of weld appearance. Through the training of the model, the accurate classification and recognition of surface defects of tube and plate welds have been achieved. The experimental results show that the improved vision-based welding surface defect recognition and classification proposed in this paper has better performance and accuracy. Up to 96.34%.

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Zhu, Xiao Gang, and Bin Long Lei. "Study on Safety Assessment Methods of the Penstock of the High Head Hydropower Station Using the China Standard GB/T 19624-2004." Advanced Materials Research 399-401 (November 2011): 2288–95. http://dx.doi.org/10.4028/www.scientific.net/amr.399-401.2288.

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Based on the finite element analysis, the general mechanical property tests and the CTOD (crack tip opening displacement) test of the penstock in the third deviated hole, the GB/T19624-2004, that is the criterion of safety assessment for in-service pressure vessels containing defects suggested by China, is applied to assess the five weld defects in the penstock, which experienced the failure of the pressure test. The assessment results indicate that 1# defect and 5# defect could be accepted, while the other defects should be repaired. According to the assessment results, the defects over criterion in the penstock must be repaired. After the penstock was repaired, it was checked by the ultrasonic flaw detection again. And the results indicate that all weld joints including circumferential and longitudinal weld joints in the penstock around the pipe close in the third deviated hole are acceptable, what provides a scientific basis for the second pressure test and the safe operation of the High head hydropower station.

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Chen, Benzhi, Zhihong Fang, Yong Xia, Ling Zhang, Yijie Huang, and Lisheng Wang. "Accurate defect detection via sparsity reconstruction for weld radiographs." NDT & E International 94 (March 2018): 62–69. http://dx.doi.org/10.1016/j.ndteint.2017.11.006.

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Hongquan, JIANG, HE Shuai, GAO Jianmin, WANG Rongxi, GAO Zhiyong, WANG Xiaoqiao, XIA Fengshe, and CHENG Lei. "An Improved Convolutional Neural Network for Weld Defect Recognition." Journal of Mechanical Engineering 56, no. 8 (2020): 235. http://dx.doi.org/10.3901/jme.2020.08.235.

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Cheng, Yuhua, Libing Bai, Fan Yang, Yifan Chen, Shenhua Jiang, and Chun Yin. "Stainless Steel Weld Defect Detection Using Pulsed Inductive Thermography." IEEE Transactions on Applied Superconductivity 26, no. 7 (October 2016): 1–4. http://dx.doi.org/10.1109/tasc.2016.2582662.

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Shiraishi, Daijiroh, Haruo Endoh, and Tsutomu Hoshimiya. "Nondestructive Evaluation of Compound Weld Defect by Photoacoustic Microscopy." Japanese Journal of Applied Physics 48, no. 7 (July 21, 2009): 07GE03. http://dx.doi.org/10.1143/jjap.48.07ge03.

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Journal articles on the topic 'Weld defect'

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