• 대한용접접합학회:학술대회논문집
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• 대한용접접합학회 2006년 추계학술발표대회 개요집
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• pp.283-285
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• 2006

Non Destructive Examination for welds is one of the most important processes to ensure the integrity of facilities of Nuclear power plants. An automated ultrasonic testing program is developed for welding inspection. A test block with side EDM notch is inspected with this program. This paper shows that the developed automated ultrasonic testing program is quite effective.

• 대한용접접합학회:학술대회논문집
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• 대한용접접합학회 2004년도 춘계 학술발표대회 개요집
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• pp.118-120
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• 2004

The non-destructive examinations for Yonggwang unit 6 was conducted in four different fields, these are 1)all non-destructive inspections for components, piping weldments and structures, 2)automated ultrasonic inspection for pressure vessels weldments. As the results, there were no big indications, and all indications detected during inspection were evaluated as the metallurgical and geometrical non-reinvent indications form weldments. Especially for the weldment of pipes, PD(Performance Demonstration) was applied as a UT inspection method according to 1995 edition of ASME code Sex. XI, this resulted in improvement of the reliability of UT inspection.

• 품질경영학회지
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• 제51권2호
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• pp.283-296
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• 2023

Purpose: The purpose of this study is to actually implement and verify whether welding defects can be detected in real time by utilizing deep learning AI solutions in the welding process of electric vehicle hairpin winding motors. Methods: AI's function and technological elements using synthetic neural network were applied to existing electric vehicle hairpin winding motor laser welding process by making special hardware for detecting electric vehicle hairpin motor laser welding defect. Results: As a result of the test applied to the welding process of the electric vehicle hairpin winding motor, it was confirmed that defects in the welding part were detected in real time. The accuracy of detection of welds was achieved at 0.99 based on mAP@95, and the accuracy of detection of defective parts was 1.18 based on FB-Score 1.5, which fell short of the target, so it will be supplemented by introducing additional lighting and camera settings and enhancement techniques in the future. Conclusion: This study is significant in that it improves the welding quality of hairpin winding motors of electric vehicles by applying domestic artificial intelligence solutions to laser welding operations of hairpin winding motors of electric vehicles. Defects of a manufacturing line can be corrected immediately through automatic welding inspection after laser welding of an electric vehicle hairpin winding motor, thus reducing waste throughput caused by welding failure in the final stage, reducing input costs and increasing product production.

• 한국구조물진단유지관리공학회 논문집
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• 제12권1호
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• pp.183-192
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• 2008

본 연구에서는 강관 말뚝 두부보강 방법으로 사용되고 있는 용접방법과 훅형방법의 성능평가를 위한 실험적 연구를 수행하였다. 실험은 강관 말뚝 두부 연결부에 작용하는 대표적 하중인 압축과 수평하중에 대한 정적 재하실험과 인장실험으로 구분하여 수행하였다. 압축하중은 강관말뚝이 부담하는 주된 하중이며, 수평력은 지진하중을 고려하여 설계할 때 필수적으로 검토하여야 한다. 또한, 강관 파일 두부연결부의 인장내력은 안전성 검토에 있어서 중요한 요인으로 고려되고 있다.강관 말뚝 두부보강 성능평가 실험에서 고려된 실험변수는 Table 1과 같이 두부보강방법인 훅형과 용접형을 고려하였다. Table 1에 실험체 일람을 나타내었다.

• 한국산업융합학회 논문집
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• 제19권1호
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• pp.42-49
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• 2016

This study proposes a non-contact inspective technology based robot vision system for Faulty Inspection of welding States and Parts Shape. The maine focus is real time implementation of the machining parts' automatic inspection by the robotic moving. For this purpose, the automatic test instrument inspects the precision components designator the vision system. pattern Recognition Technologies and Precision Components for vision inspection technology and precision machining of precision parts including the status and appearance distinguish between good and bad. To perform a realization of a real-time automation integration system for the precision parts of manufacturing process, it is designed a robot vision system for the integrated system controller and verified the reliability through experiments. The main contents of this paper, the robot vision technology for noncontact inspection of precision components and machinery parts is useful technology for FA.

• 비파괴검사학회지
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• 제23권1호
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• pp.38-44
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• 2003

원자력발전소의 안전성등급 기기에 적용되는 비파괴검사는 실제 결함을 실현한 시험편을 사용하여 결함탐지능력을 검증하도록 하는 기량검증이 요구되고 있다. 가동중인 원전에서 발생 가능한 균열으로는 기계적 피로균열, 열 피로균열 및 입계부식균열 등이 있으나 본 연구에서는 기계적 피로균열을 대상으로 하였다. 인장 피로하중을 사용하여 기계적 피로결함을 제조하기 위해서 시험편을 설계하였고 원하는 피로결함 파면의 조도를 얻기 위해서 인가하중의 크기 및 사이클 수를 조절하여 피로결함을 발생시켰다. 발생된 결함에 대한 정확한 크기와 위치에 대한 물리적 정보를 얻은 후에 결함이 설계된 크기와 위치에 존재하도록 기밀용접을 실시하였다. 기밀용접 후 잔여 용접 흠은 가스 텅스텐 아크용접 및 플럭스 코어드 아크용접으로 채워졌다. 최종 완성된 피로결함 시험편을 방사선투과검사 및 초음파탐상검사를 통하여 검사한 결과, 설계된 길이와 깊이로 피로결함이 형성되었음을 확인하였다.

• 대한기계학회논문집A
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• 제22권3호
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• pp.644-653
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• 1998

In resistance spot weld, the assurance of weld quality has been a long-standing problem. Since the weld nuggets if resustance spot welding form between the workpieces, visual detection of defects in usually impossible. Welding quality of resistance spot welding can be verified by non destructive and destructive inspections such as X-Ray inspection and testing of weld strength. But these tests, in addition to being time-consuming and costly, can entail risks due to sampling basis. The purpose of this study is the development of the monitoring system based on fuzzy inference, aimed at diagonosis of quality in resistance spot welding. The fuzzy inference system consists of fuzzy input variables, fuzzy membership functions and fuzzy rules. For inferring the welding quality(strength), the experimental data of the spot welding were acquired in various welding conditions with the monitoring system designed. Some fuzzy input variables-maximum, slop and difference values of electrode movement signals-were extracted from the experimental data. It was confirmed that the fuzzy inference values of strength have a .${\pm}$5% error in comparison with actual values for the selected welding conditions(9-10.5KA, 10-14 cycle, 250-300 $kg_f$). This monitoring system can be useful in improving the quality assurance and reliability of the resistance spot welding process.

• Journal of Welding and Joining
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• 제7권4호
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• pp.30-37
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• 1989

Effect of heat treatment on mechanical properties of an overlay weldment was investigated. Over welding was carried out on the structural C-Mn mild steel substrate to take required test specimens. Shielded metal arc welding process with 13Cr-0.2Ni stick electrode was applied. The heat treatment temperatures and holding times were $450{\circ}C., 550{\circ}C., 650{\circ}C., 750{\circ}C., 850{\circ}C.$ and 0.5hr, 2hr, 10hr, respectively. Mechanical tests and microscopic inspection were also carried out to investigate welds soundness. Test results indicated that carbon migration was dominant near bonded zone. At temperature of around 650.deg. C, carburized layer and decarburized layer were formed remarkably along overlay welds region and C-Mn mild steel region, respectively. The wideth of these layers became wider with increasing heat treatment temperature and/or holding time at the elevated temperature, and this relationship agreed with Larson-Miller parameter. Side bending test results demonstrated that the crack free region of overlay welds could be deduced from the relationship between temperature and holding time.

• 한국정밀공학회지
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• 제16권10호
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• pp.141-151
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• 1999

The resistance Spot Welding is widely used in the field of assembling the plates. However we don't still have any satisfactory solution, which is non-destructive quality evaluation in real-time or on-line, against it. Moreover, even though the rate of welding under the condition of expulsion has been high until now, quality control of welding against expulsion hasn't still been established. In this paper, it was proposed on the quality assurance technique of resistance spot welding pieces using Neuro-Fuzzy algorithm. Four parameters from electrode separation signal in the case of non-expulsion, and dynamic resistance patterns in the case of expulsion are selected as fuzzy input parameters. The parameters consist of Fuzzy Inference System are determined through Neuro-Learning algorithm. And then, fuzzy Inference System is constructed. It was confirmed that the fuzzy inference values of strength have within ${\pm}$4% error specimen in comparison with real strength for the total strength range, and the specimen percent having within ${\pm}$1% error was 88.8%. According to KS(Korean Industrial Standard), tensile-shear strength limit for electric coated of zinc is 400kgf/mm2. Judging to the quality of welding is good or bad, according to this criterion and the results of inference, the probability of misjudgement that good quality is valuated into poor one was 0.43%, on contrary it was 2.59%. Finally, the proposed Neuro-Fuzzy Inference System can infer the tensile-shear strength of resistance spot welding pieces with high performance for all cases-non-expulsion and expulsion. And On-Line Welding Quality Inspection System will be realized sooner or later.

• 한국정밀공학회지
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• 제19권11호
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• pp.103-111
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• 2002

In this study, the natural flaws in welding parts are classified using the signal pattern classification method. The storage digital oscilloscope including FFT function and enveloped waveform generator is used and the signal pattern recognition procedure is made up the digital signal processing, feature extraction, feature selection and classifier design. It is composed with and discussed using the distance classifier that is based on euclidean distance the empirical Bayesian classifier. feature extraction is performed using the class-mean scatter criteria. The signal pattern classification method is applied to the signal pattern recognition of natural flaws.