• Journal of the Korea Institute of Information and Communication Engineering
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• v.13 no.2
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• pp.407-412
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• 2009

Recently, in Weld Processing field, unmanned and automatic system construction has experienced the rapid growth, and diverse signal processing has been employed in order to translate the exact weld pattern. In this paper, We will suggest the effective neural network which can decide the weld quality in arc weld and monitoring system in real time. In addition, We will present the pre-processing for selecting the study data, and the method to evaluate the wave of weld more precisely and accurately through known Neural Network.

• Journal of the Institute of Convergence Signal Processing
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• v.23 no.4
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• pp.241-248
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• 2022

Shear Reinforcement of Dual Anchorage(SRD) is used to reinforce the safety of reinforced concrete structures at construction sites. Welding is used to make shear reinforcement, and welding plays an important role in determining productivity and competitiveness of products. Therefore, a weld bead detection inspection is required. In this paper, we suggest an algorithm for inspecting welding beads using image data of welding beads. First, the proposed algorithm calculates a brightness value in a vertical direction in an image, and then divides a welding bead in a vertical direction by finding a position corresponding to a 50% height point of the brightness value distribution in the image. The welding bead area is also divided in the same way for the horizontal direction, and then the segmentation image is analyzed if there is a welding bead. The proposed algorithm reduced the amount of computation by performing analysis after specifying the region of interest. In addition, accuracy could be improved by using all brightness values in the vertical and horizontal directions using the difference of brightness between the base metal and the welding bead region in the SRD image. The experiment compared the analysis results using five algorithms, such as K-mean and K-neighborhood, as a method to detect if there is a welding bead, and the experimental result proved that the proposed algorithm was the most accurate.

• Journal of the Korean Society for Nondestructive Testing
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• v.29 no.5
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• pp.473-478
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• 2009

The purpose of this study is to evaluate the behaviour of the plastic deformed zone at crack tip on the standard Charpy specimens which were taken from the multi-passed weld block of the pressure vessel steel. Notch was machined on the standard Charpy test specimens and pre-crack which was located around the fusion line was made under the repeat load. Four point bend and acoustic emission tests were carried out simultaneously. The size of plastic region at crack tip was calculated using stress intensity factor. Relationships between characteristics of acoustic emission and plastic zone size at crack tip were discussed through the cumulative AE energy. Regardless of the specimens, AE signals were absent within the elastic region almost and most of AE signals were produced at the plastic deformation region from yield point to the mid-point between yield and maximum load. More AE signals for the weldment were produced compared with the base-metal and PWHT specimen. Relations between plastic deformed zones at crack tip and cumulative AE energy for the weldment and PWHT specimen were different quietly from the base-metal. Besides, number of AE counts for the weldment was the larger than those of the base-metal and PWHT specimen.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.9 no.6
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• pp.1379-1386
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• 2005

For efficient welding it is necessary to maintain stability of the welding process and control the shape of the welding bead. The welding quality can be controlled by monitoring important parameters, such as, the Arc Voltage, Welding Current and Welding Speed during the welding process. Welding systems use either a vision sensor or an Arc sensor, both of which are unable to control these parameters directly. Therefore, it is difficult to obtain necessary bead geometry without automatically controlling the welding parameters through the sensors. In this paper we propose a novel approach using fuzzy logic and neural networks for improving welding qualify and maintaining the desired weld bead shape. Through experiments we demonstrate that the proposed system can be used for real welding processes. The results demonstrate that the system can efficiently estimate the weld bead shape and remove the welding detects.

• Journal of the Institute of Convergence Signal Processing
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• v.22 no.3
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• pp.141-148
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• 2021

In this paper, we propose an algorithm of welding bead detection and evaluation using geodesic active contour algorithm and high pass filter with image processing technique. The algorithm uses histogram equalization and high pass filter as gaussian filter to improve contrast. The image processing techniques smoothens the welding beads reduce the noise on an image. Then, the algorithm detects the welding bead area by applying the geodesic active contour algorithm and morphological ooperation. It also applies the balloon force that either inflates in, or deflates out the evolving contour for a better segmentation. After that, we propose a method for determining the quality of welding bead using effective length and width of the detected bead. In the experiments, our algorithm achieved the highest recall, precision, F-measure and IOU as 0.9894, 0.9668, 0.9780, and 0.8957 respectively. We compared the proposed algorithm with the conventional algorithms to evaluate the performance of the proposed algorithm. The proposed algorithm achieved better performance compared to the conventional ones with a maximum computational time of 0.6 seconds for segmenting and evaluating one welding bead.

• Journal of the Korean Society for Nondestructive Testing
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• v.16 no.1
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• pp.34-45
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• 1996

The signal pattern recognition program which can perform signal acquisition and processing, the extraction and selection of features, the classifier design and the evaluation, is developed and applied to the classification of artificial defects in the weld zone of Austenitic STS304. The neural network classifier is compared with the linear discriminant function classifier and the empirical Bayesian classifier. The signal through a broadband sensor is compared with that through a resonance type sensor. In recognition rate, the neural network classifier is best, and the signal through a broadband sensor is better.

• Journal of Welding and Joining
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• v.8 no.3
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• pp.70-78
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• 1990

The harsh nature of welding environments makes welding a prime candidate for process automation. Among the variety of welding processes available, gas metal arc welding is one of the most frequently used methods, primarily because it is highly suited to a wide range of applications, and also to automation. Automatic seam tracking method is one of the most demanded techniques for automatic control of arc welding. In this study a seam tracking system has been developed by using the welding arc itself as a sensor. This paper described the principle and experimental result of the arc sensor system, as well as the development and application of the automatic CO_2$ welding for the horizontal fillet welding. A basic problem in horizontal fillet welding is the prevention of hanging bead formation such as undercut at the vertical plate and overlap at the horizontal plate. To produce the symmetric bead shape, the relationship of bead shape to welding parameters(welding velocity, weaving width, weaving speed, tip to workpiece distance) was also investigated.

• Journal of the Korean Society for Nondestructive Testing
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• v.21 no.1
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• pp.54-61
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• 2001

A software package to classify acoustic emission (AE) signals using the wavelet transform and the neural network was developed Both of the continuous and the discrete wavelet transforms are considered, and the error back-propagation neural network is adopted as m artificial neural network algorithm. The signals acquired during the 3-point bending test of specimens which have artificial defects on weld zone are used for the classification of the defects. Features are extracted from the time-frequency plane which is the result of the wavelet transform of signals, and the neural network classifier is tamed using the extracted features to classify the signals. It has been shown that the developed software package is useful to classify AE signals. The difference between the classification results by the continuous and the discrete wavelet transforms is also discussed.

• Journal of the Korean Society for Nondestructive Testing
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• v.17 no.1
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• pp.11-22
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• 1997

It is very essential to get the accurate classification of defects in primary pressure vessel and piping welds for the safety of nuclear power plant. Ultrasonic testing has been widely applied to inspect primary pressure vessel and piping welds of nuclear power plants during PSI / ISI. Classification of flaws in weldments from their ultrasonic scattering signals is very important in quantitative nondestructive evaluation. This problem is ideally suited to a modern ultrasonic Pattern recognition technique. Here, a brief discussion on systematic approach to this methodology is presented including ultrasonic feature extraction, feature selection and classification. A stronger emphasis is placed on Fuzzy-UTSCS (UT signal classification system) as efficient classifiers for many practical classification problems. As an example Fuzzy-UTSCS is applied to classify flaws in ferrite pressure vessel weldments into two types such as linear and volumetric. It is shown that Fuzzy-UTSCS is able to exhibit higher performance than other classifiers in the defect classification.

• Journal of Welding and Joining
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• v.32 no.6
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• pp.64-69
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• 2014

The applications by using fiber laser have increased recently. However, due to high beam quality of fiber laser, it is inappropriate to apply the existing laser welding monitoring technology to the fiber laser welding as it is. On this study, thus, we analyzed emission signal with RMS and FFT for the in-process monitoring during fiber laser welding. 12mm-thick 304L stainless steel sheet was used in fiber laser welding and the result showed as follows: The intensity changes in RMS did not clarify the distinction between full penetration and partial penetration. However, as welding speed increases, specific frequency also increases in regards of frequency analysis by using FFT.