• 제어로봇시스템학회:학술대회논문집
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• 1997.10a
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• pp.52-55
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• 1997

This paper discusses an automatic visual-servoing system, in which a laser and a CCD camera are used for imaging the pattern of joint groove. The algorithm used here is simple and robust to find out the gap width and gap center. As a consequence, the speed of algorithm is very fast and optimized. A feature of this system is that it processes only by summing the vertical line and horizontal line of screen without any image preprocessing in order to get the energy information of lines alternatively. It is practical and useful for the system requiring a fast process time of vision.

• Journal of the Society of Naval Architects of Korea
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• v.55 no.1
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• pp.66-74
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• 2018

In order to evaluate the precision degree of the blocks on the dock, the shipyards recently started to use the point cloud approaches using the 3D scanners. However, they hesitate to use it due to the limited time, cost, and elaborative effects for the post-works. Although it is somewhat traditional instead, they have still used the electro-optical wave devices which have a characteristic of having less dense point set (usually 1 point per meter) around the contact section of two blocks. This paper tried to expand the usage of point sets. Our approach can estimate the rework time to weld between the Pre-Erected(PE) Block and Erected(ER) block as well as the precision of block construction. In detail, two algorithms were applied to increase the efficiency of estimation process. The first one is K-mean clustering algorithm which is used to separate only the related contact point set from others not related with welding sections. The second one is the Concave hull algorithm which also separates the inner point of the contact section used for the delayed outfitting and stiffeners section, and constructs the concave outline of contact section as the primary objects to estimate the rework time of welding. The main purpose of this paper is that the rework cost for welding is able to be obtained easily and precisely with the defective point set. The point set on the blocks' outline are challenging to get the approximated mathematical curves, owing to the lots of orthogonal parts and lack of number of point. To solve this problems we compared the Radial based function-Multi-Layer(RBF-ML) and Akima interpolation method. Collecting the proposed methods, the paper suggested the noble point matching method for minimizing the rework time of block-welding on the dock, differently the previous approach which had paid the attention of only the degree of accuracy.

• Proceedings of the Korean Society of Laser Processing Conference
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• 1998.11a
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• pp.0-0
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• 1998

-Part I- As market needs become more various, the production of smaller quantities of a wider variety of products becomes increasingly important. In addition, in order to meet demands for more efficient production, long-term unmanned factory operation is prevailing at a remarkable pace. Within this context, laser machines are gaining increasing popularity for use in applications such as cutting and welding metallic and ceramic materials. FANUC supplies four models of $CO_2$ laser oscillators with laser power ranging from 1.5㎾ to 6㎾ on an OEM basis to machine tool builders. However, FANUC has been requested to produce laser oscillators that allow more compact and lower-cost laser machines to be built. To meet such demands, FANUC has developed six models of Slab type YAG laser oscillators with output power ranging from 150W to 2㎾. These oscillators are designed mainly fur cutting and welding sheet metals. The oscillator has an exceptionally superior laser beam quality compared to conventional YAG laser oscillators, thus providing significantly improved machining capability. In addition, the laser beam of the oscillator can be efficiently transmitted through quartz optical fibers, enabling laser machines to be simplified and made more compact. This paper introduces the features of FANUC’s developed Slab type YAG laser oscillators and their applications. - Part II - All-solid-state lasers employing laser diodes (LD) as a source of pumping solid-state laser feature high efficiency, compactness, and high reliability. Thus, they are expected to provide a new generation of processing tools in various fields, especially in automobile and aircraft industries where great hopes are being placed on laser welding technology for steel plates and aluminum materials for which a significant growth in demand is expected. Also, in power plants, it is hoped that reliability and safety will be improved by using the laser welding technology. As in the above, the advent of high-power all-solid-state lasers may not only bring a great technological innovation to existing industry, but also create new industry. This is the background for this project, which has set its sights on the development of high-power, all-solid-state lasers with an average output of over 10㎾, an oscillation efficiency of over 20%, and a laser head volume of below 0.05㎥. FANUC Ltd. is responsible for the research and development of slab type lasers, and TOSHIBA Corp. far rod type lasers. By pumping slab type Nd: YAG crystal and by using quasi-continuous wave (QCW) type LD stacks, FANUC has already obtained an average output power of 1.7㎾, an optical conversion efficiency of 42%, and an electro-optical conversion efficiency of 16%. These conversion efficiencies are the best results the world has ever seen in the field of high-power all-solid-state lasers. TOSHIBA Corp. has also obtained an output power of 1.2㎾, an optical conversion efficiency of 30％, and an electro-optical conversion efficiency of 12%, by pumping the rod type Nd: YAG crystal by continuous wave (CW) type LD stacks. The laser power achieved by TOSHIBA Corp. is also a new world record in the field of rod type all-solid-state lasers. This report provides details of the above results and some information on future development plans.

• Proceedings of the Korean Society of Computer Information Conference
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• 2009.01a
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• pp.9-12
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• 2009

최근 무인 로봇 및 산업 자동화의 비약적인 발전으로 용접 분야에서도 무인화 및 자동화 시스템 구축이 활성화 되고 있다. 본 논문에서는 아크 용접 시스템의 주요한 용접 인자인 용접전류, 용접전압 정보를 PSoC 기반의 WirelessUSB를 이용하여 무선으로 모니터링 시스템에 전송하고 이를 신경회로망에 적용하여 용접 현상을 모니터링 하였다. 또한 산업 현장에도 일반화된 TCP/IP 통신을 이용하여 원격으로 관리가 가능하도록 구현하였다.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2008.10a
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• pp.520-523
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• 2008

As a result of the recent development of the electro-information industry, the hardware of an automated ultrasonic testing system is getting lighter and diversified image processing techniques are applied to its software so that the possible precise totaling and detecting of the flaws are studied. This study proposes an automated ultrasonic testing system of the pipe in order to organize the optimized system, and also describes the data flow and general composition of the software for the design of the system.

• Journal of the Institute of Electronics and Information Engineers
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• v.49 no.9
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• pp.353-361
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• 2012

In this paper, we propose a improvement method of vision inspector for T type welding nut using an auto sorting system. We used edge and thread detection with histogram of image which is captured by machine vision camera. We also used a binary morphology operation for a detection of spot. A major problem in this vision inspector is abnormal operation caused by degradation of image acquired. These degradations caused by oil pollution on conveyer belt. For overcome this problem, we introduce a pre-processing using a masked histogram equalization on the image acquired. Histogram equalization is applied on masked region (nut part) for increase contrast. As a result, we can remove features caused by oil pollution on background and reduce a ratio of abnormal operation from 10.0 % to 0.2 %.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.7 no.5
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• pp.1019-1025
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• 2003

There are many kinds of seam trackers in the industrial welding field. We are proposed the contact seam tracking sensor applying strain gauges kind of contact sensor that mostly used in welding part now. For this seam tracking experiment, we made the strain gauges sensor by ourselves and tested how well the sensor tracks the seam. The experiment device consist of strain gauges sensor, amplifier circuit of strain gauges signal, saw wave generator, MOSFET power diving circuit and X-Y slide by moved DC motor. The tracking areas are X-Y planes(left, right, up and down) and the change of strain gauge resistance causes to move DC motor that connected to X-Y slide. As a result of experiment, we confirmed that the strain gauges sensor tracks a seam well, and X-Y slide DC motor was controlled by PWM control.

• 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 the Korea Institute of Information and Communication Engineering
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• v.25 no.4
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• pp.515-522
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• 2021

In this paper, we present and analyze a method of applying a machine learning to ultrasonic test signals for constant monitoring of the welding faults in a tubular steel tower. For the machine learning, feature selection based on genetic algorithm and fault signal classification using a support vector machine have been used. In the feature selection, the peak value, histogram lower bound, and normal negative log-likelihood from 30 features are selected. Those features clearly indicate the difference of signals according to the depth of faults. In addition, as a result of applying the selected features to the support vector machine, it has been possible to perfectly distinguish between the regions with and without faults. Hence, it is expected that the results of this study will be useful in the development of an early detection system for fault growth based on ultrasonic signals and in the energy transmission related industries in the future.

• Journal of Korea Society of Industrial Information Systems
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• v.29 no.1
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• pp.107-116
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• 2024

Copper Filter Dryer (CFD) are responsible for removing impurities from the circulation of refrigerant in refrigeration and cooling systems to maintain clean refrigerant, and defects in CFD can lead to product defects such as leakage and reduced lifespan in refrigeration and cooling systems, making quality assurance essential. In the quality inspection stage, human inspection and defect judgment methods are traditionally used, but these methods are subjective and inaccurate. In this paper, YOLOv7 object detection algorithm was used to detect defects occurring during the CFD Shaft pipe and welding process to replace the existing quality inspection, and the detection performance of F1-Score 0.954 and 0.895 was confirmed. In addition, the cause of defects occurring during the welding process was analyzed by analyzing the sensor data corresponding to the Timestamp of the defect image. This paper proposes a method for manufacturing quality assurance and improvement by detecting defects that occur during CFD process and analyzing their causes.