• Journal of Advanced Research in Ocean Engineering
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• v.1 no.3
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• pp.148-156
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• 2015

Deck plates of ships or offshore structures would make out-of-plane distortion for their thin thickness. These distortions are usually straightened by thermal straightening such as flame heating method. After thermal straightening, the blocks are lifted and moved by cranes to assemble it at dry-dock stage. After this lifting process, out-of-plane deformation again happens frequently. And then, they continuously cause quality and accuracy problems in the final dry-dock process. So, it takes more time for repair and correction working. According to preceding research, the lifting process by cranes would offset the effect on thermal straightening. The target of this study is to develop a methodology analyzing the remaining efficiency of thermal straightening after block lifting. The development was based on the assumption of yield state at straightening region. Therefore the remaining efficiency was obtained by different stiffness slope while lifting & relieving. The efficiency formula was designed using inherent strain, and we made a table of zero-efficiency by cooling speed and class rule's steels. As a result, if the stress orthogonal to straightened line is calculated during lifting analysis by FEA, the efficiency can be obtained linearly to the values in the table. Finally, even optimized carling position can be designed by considering the regional data from series project and welding region on deck.

• Transactions of the Korean Society of Mechanical Engineers
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• v.17 no.6
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• pp.1423-1430
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• 1993

One of the most important parameters for explosive welding is flyer velocity $V_p$, which principally depends on momentum caused by detonation of explosive. And close dependency with other parameters such as detonation velocity $V_D$, dynamic angle $\beta$, charge ratio R, flyer thickness $t_f$ and stand-off distance d, should be taken accounts for welding design. This paper describes, as a result of experiment, an empirical equation related to relation between $V_p$/$V_D$ and R. The flyer velocity which is estimated by $V_{p}=0.284{\times}R^{0.593}$or $V_{p}=\sqrt[0.2]{2E_G}{\times}R^{0.593}$ can be used in ordinary experiments. And the calculated values of the flyer velocity exhibit better accuracy than those of other investigators.

• 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 Welding and Joining
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• v.16 no.3
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• pp.64-73
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• 1998

This paper is concerned with defects detection and evaluation of heat affected zone (HAZ) in austenitic stainless steel type 304 by ultrasonic wave and neural network. In experiment, the reflected ultrasonic defect signals from artificial defects (side hole, vertical hole, notch) of HAZ appears as beam distance of prove-defect, distance of probe-surface, depth of defect-surface on CRT. For defect classification simulation, neural network system was organized using total results of ultrasonic experiment. The organized neural network system was learned with the accuracy of 99%. Also it could be classified with the accuracy of 80% in side hole, and 100% in vertical hole, 90% in notch about ultrasonic pattern recognition. Simulation results of neural network agree fairly well with results of ultrasonic experiment. Thus were think that the constructed system (ultrasonic wave - neural network) in this work is useful for defects dection and classification such as holes and notches in HAZ of austenitic stainless steel 304.

• Journal of the Society of Naval Architects of Korea
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• v.39 no.1
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• pp.49-60
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• 2002

Welding is essential in ship production since welding is very popular method for joining two or more metals. However, welding causes residual stress and distortion and these give a bad influence to the structure strength and assembly of ship blocks. Therefore, prediction and treatment of residual stress and distortion is a key to accuracy control in shipyard. In this paper, a computational procedure, based on thermal-elastic-plastic 3-dimensional FEA, has been suggested to simulate butt and fillet welding process. In the simulation process, temperature distribution at each time step is obtained by heat transfer analysis and then thermal deformation analysis is done with obtained temperature distributions to find the residual stress and distortion. In heat transfer analysis, enthalpy method is used to realize phase change at melting temperature. Also element birth and death method is used to simulate adding of weld metal in both heat transfer analysis and thermal elastic plastic analysis. The proposed procedure is verified by related researches and the results show good agreement with those of related researches.

• Journal of Korean Society of Occupational and Environmental Hygiene
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• v.9 no.2
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• pp.135-144
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• 1999

The purposes of this study were to survey types of pretreatment methods adopted by industrial hygiene laboratories in Korea for extracting heavy metals in welding fume samples and to compare performances of two pretreatment methods, the acid extraction and the microwave digestion, in extracting heavy metals contained in the real workplace samples from various welding jobs including arc, argon, and carbon dioxide. A total of 25 analytical chemists in the industrial hygiene laboratories participating the quality control program directed by the Korea Industrial Safety Corporation were interviewed by telephone. For the purpose of comparing performance of extracting heavy metals from real workplace samples, a total of 53 welders from 21 workplaces located in Anyang, Uiwang, and Kunpo areas were sampled from the period of March 22, 1999 to April 20, 1999. It was found that the most frequently adopted method for samples from the quality control program was the acid extraction method(40%) followed by the NIOSH 7300 method(36%). The NIOSH method, however, was the dominant method(36%) for samples from workplace followed by the acid extraction method(28%). In this study, two extraction methods, the acid extraction and the microwave digestion, were compared in terms of recovery rate, accuracy, and precision for both manganese and chromium. Both methods produced comparable results for the samples prepared for the quality control program. In contrast, concentrations of two heavy metals determined from real workplace samples pretreated with the microwave digestion method were statis tically significantly higher, manganese(166%) and chromium (200%), than those of utilizing the acid extraction method. These findings were consistent regardless of types of welding techniques used. The results of this study clearly show the importance of verifying the analytical performances of extraction methods for heavy metals not only for the samples from the quality control program but also from the real world samples collected from welding jobs.

• Journal of Digital Convergence
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• v.17 no.5
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• pp.217-223
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• 2019

A study in predicting defects of spot welding in real time in automotive field is essential for cost reduction and high quality production. Welding quality is determined by shear strength and the size of the nugget, and results depend on different independent variables. In order to develop the real-time prediction system, multiple regression analyses were conducted and the two dependent variables were obtained with sufficient statistical results with three independent variables, however, the quality prediction by the regression formula could not ensure accuracy. In this study, a multi-layer neural network circuit was constructed. The neural network by 10 dynamic resistance variables was constructed with three hidden layers to obtain execution functions and weighting matrix. In this case, the neural network was established with three independent variables based on regression analysis, as there could be difficulties in real-time control due to too many input variables. As a result, all test data were divided into poor, partial, and modalities. Therefore, a real-time welding quality determination system by three independent variables obtained by multiple regression analysis was completed.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.14 no.3
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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.

• Journal of Welding and Joining
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• v.27 no.5
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• pp.88-93
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• 2009

While the pinch instability theory (PIT) has been widely employed to analyze the spray transfer mode in the gas metal arc welding (GMAW), it cannot predict the detaching drop size accurately. The PIT is modified in this work to increase the accuracy of prediction and to simulate the molten tip geometry to be more physically acceptable. Since the molten tip becomes a cone shape in the spray mode, the effective wire diameter is formulated that the effective diameter is inversely proportional to current square. Modifications are also made to consider the finite length of the liquid column and current leakage through the arc. While the effective diameter influences drop transfer significantly, the current leakage has negligible effects. The effects of modifications on drop transfer are analyzed, and the predicted drop diameters show good agreements with the experimental data of the steel wire.

• 제어로봇시스템학회:학술대회논문집
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• 2005.06a
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• pp.643-648
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• 2005

Generally, ships have marks of various shapes on outside of the hull. Among them, so called "Draft Mark" indicates the distance from the bottom of the keel to the waterline. Draft marks are used to determine the displacement and other properties of the ship for stability and control purposes. These marks are made up of welding bead or sticking the steel plate on outside of the hull. To improve the confidence level of the ship owner, quality and accuracy of the draft mark is very important. So the automatic mark welding robot is used to enable a high quality and accurate manufacturing line. To improve the system portability, the system is divided into two distinct parts, namely mechanical part and control part. Mechanical part is robust, a lightweight, and easy to dismantle. The control part consists of an in-house developed controller, which is based on embedded Linux. Also, the control part consists of power line communication module to ensure the applicability of the controller in manufacturing line. In this paper, the methodologies of control and configuration of the robot are discussed.