• Nuclear Engineering and Technology
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• 제53권11호
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• pp.3852-3859
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• 2021

This paper presents experimental and numerical analysis results regarding the effects of an incomplete penetration defect on the fatigue lives of socket welded pipes. For the experiment, four-point bending fatigue tests with various defect geometries (defect depth and circumferential length) were performed, and test results are presented in terms of stress-life data. The results showed that for circumferentially short defects, the fatigue life tends to increase with increasing crack depth, but for longer defects, the trend becomes the opposite. Finite element analysis showed that for short defects, the maximum principal stress decreases with increases in crack depth. For a longer defect, the opposite trend was found. Furthermore, the maximum principal stress tends to increase with an increase in defect length regardless of the defect depth.

• Journal of Welding and Joining
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• 제18권3호
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• pp.68-75
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• 2000

The incomplete penetration(IP) discontinuity in butt joint weld, which is detected during safety analysis of the steel bridge by nondestructive evaluation(NDE) method, is classified as unacceptable defect according to the NDE codes and standards. In fact, there are a little allowance in butt joint weld in the view point of design criterion, and also detected IP discontinuity should be classified and evaluated for the fatigue strength and residual life estimation. In this study, we performed fatigue test to evaluate the fatigue strength of high strength steel(SWS490A) containing IP discontinuities in various size, the results compared and classified according to the bridge construction standard specification which published by korea administration of construction and traffic. Experimental results could be used to evaluate and estimate the IP discontinuities considering different stress range in butt joint bridge weld during periodic safety inspection.

• 한국정밀공학회지
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• 제15권6호
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• pp.167-174
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• 1998

The ultrasonic test is recognized for its significance as a non-destructive testing method to detect volume defects such as porosity and incomplete penetration which reduce strength in the weld zone. This paper illustrates the defect detection in the weld zone of ferritic carbon steel using ultrasonic wave and the evaluation of pattern recognition by chaotic feature extraction using time series signal of detected defects as data. Shown in the time series data were that the time delay was 4 and the embedding dimension was 6 which indicate the geometric dimension of the subject system and the extent of information correlation. Based on fractal dimension and lyapunov exponent in quantitative chaotic feature extraction, feature value of 2.15, 0.47 is presented for porosity and 2.24, 0.51 for incomplete penetration The precision rate of the pattern recognition is enhanced with these values on the total waveform of defect signal in the weld zone. Therefore, we think that the ultrasonic pattern recognition method of weld zone defects of ferritic carbon steel by ultrasonic-chaotic feature extraction proposed in this paper can boost precision rate further than the existing method applying only partial waveform.

• Steel and Composite Structures
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• 제17권6호
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• pp.851-865
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• 2014

Full penetration welded steel moment-resisting frame (SMRF) structures with welded box sections are widely employed in steel bridges, where a large number of steel bridges have been in operation for over fifty years in Japan. Welding defects such as incomplete penetration at the beam-column connections of these existing SMRF steel bridge piers were observed during inspection. Previous experiments conducted by the authors' team indicate that gusset stiffeners (termed fillets in this study) at the beam-web-to-column-web joint of the beam-column connections may play an important role on the seismic performance of the connections. This paper aims to experimentally study the effect of the fillet radius on seismic performance of the connections with large welding defects. Four specimens with different sizes of fillet radii were loaded under quasi-static incremental cyclic loading, where different load-displacement relations and cracking behaviors were observed. The experimental results show that, as the size of the fillet radius increases, the seismic performance of the connections can be greatly improved.

• Journal of Welding and Joining
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• 제14권6호
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• pp.58-67
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• 1996

The horizontal filet welding is prevalently used in heavy and ship building industries to join the parts. The phenomena occurring in the horizonal fillet welding process are very complex and highly non-linear, so that its analysis is relatively difficult. Furthermore, various kinds of weld defect such as undercut, overlap, porosity. excess weld metal and incomplete penetration can be induced due to improper welding conditions. Among these defects, undercut, overlap and excess weld metal appear frequently in horizontal filet welding. To achieve a satisfactory weld bead geometry without weld defects, it is necessary to study the effect of welding conditions in the weld bead geometry. For analyzing the weld bead geometry with and without weld defects in horizontal fillet welding, a mathematical model was proposed in conjunction with a two-dimensional heat flow analysis adopted for computing the melting tone in . base metal. The reliability of the proposed model was evaluated through experiments. which showed that the proposed model was very effective for predicting the weld bead shape with or without weld defects in horizontal fillet welding.

• 대한기계학회논문집A
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• 제23권7호
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• pp.1065-1074
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• 1999

This study proposes the analysis and evaluation method of time series ultrasonic signal using the chaotic feature extraction for ultrasonic pattern recognition. Features extracted from time series data using the chaotic time series signal analysis quantitatively welding defects. For this purpose analysis objective in this study is fractal dimension and Lyapunov exponent. Trajectory changes in the strange attractor indicated that even same type of defects carried substantial difference in chaoticity resulting from distance shills such as 0.5 and 1.0 skip distance. Such differences in chaoticity enables the evaluation of unique features of defects in the weld zone. In experiment fractal(correlation) dimension and Lyapunov exponent extracted from 6dB ultrasonic defect signals of weld zone showed chaoticity. In quantitative chaotic feature extraction, feature values(mean values) of 4.2690 and 0.0907 in the case of porosity and 4.2432 and 0.0888 in the case of incomplete penetration were proposed on the basis of fractal dimension and Lyapunov exponent. Proposed chaotic feature extraction in this study enhances ultrasonic pattern recognition results from defect signals of weld zone such as vertical hole.

• 대한기계학회논문집A
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• 제20권4호
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• pp.1309-1319
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• 1996

The research for the classification of the natural defects in welding zone is performd using the neuro-pattern recognition technology. The signal pattern recognition package including the user's defined function is developed to perform the digital signal processing, feature extraction, feature selection and classifier selection, The neural network classifier and the statistical classifiers such as the linear discriminant function classifier and the empirical Bayesian calssifier are compared and discussed. The neuro-pattern recognition technique is applied to the classificaiton of such natural defects as root crack, incomplete penetration, lack of fusion, slag inclusion, porosity, etc. If appropriately learned, the neural network classifier is concluded to be better than the statistical classifiers in the classification of the natural welding defects.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제14권3호
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• pp.1086-1103
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• 2020

The automatic identification and classification of image-based weld defects is a difficult task due to the complex texture of the X-ray images of the weld defect. Several depth learning methods for automatically identifying welds were proposed and tested. In this work, four different depth convolutional neural networks were evaluated and compared on the 1631 image set. The concavity, undercut, bar defects, circular defects, unfused defects and incomplete penetration in the weld image 6 different types of defects are classified. Another contribution of this paper is to train a CNN model "RayNet" for the dataset from scratch. In the experiment part, the parameters of convolution operation are compared and analyzed, in which the experimental part performs a comparative analysis of various parameters in the convolution operation, compares the size of the input image, gives the classification results for each defect, and finally shows the partial feature map during feature extraction with the classification accuracy reaching 96.5%, which is 6.6% higher than the classification accuracy of other existing fine-tuned models, and even improves the classification accuracy compared with the traditional image processing methods, and also proves that the model trained from scratch also has a good performance on small-scale data sets. Our proposed method can assist the evaluators in classifying pipeline welding defects.