• 대한기계학회:학술대회논문집
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• 대한기계학회 2001년도 춘계학술대회논문집C
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• pp.94-99
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• 2001

Theoretical analysis using finite element method are peformed in order to clarify the formation of the flare-shape defect for multi-step tube sinking process. The parameters of concern were the friction between the tube and the die, and geometrical parameters, such as the die inclination angle, the diameters of the die entrance and exit, and the curvature at the corner of the die exit. The effect of the curvature at the comer of the die exit is dominant for determining the flare-shape defect. In order to minimize the flare-shape defect the curvature at the corner of the die exit should be increased up to a certain level(120mm). Using three-step tube sinking die sets which have different curvatures at the comer of the die exit, several numbers of tests were performed and its results are compared with that of theoretical analysis.

• Steel and Composite Structures
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• 제19권1호
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• pp.131-152
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• 2015

Easy detection and evaluation of defect in the tube structure is a continuous problem and remains a significant demand in tube inspection technologies. This study is aimed to automate defect detection using the pattern recognition approach based on the classification of high frequency stress wave signals. The stress wave signals from vibrational impact excitation on several tube conditions were captured to identify the defect in ASTM A179 seamless steel tubes. The variation in stress wave propagation was captured by a high frequency sensor. Stress wave signals from four tubes with artificial defects of different depths and one reference tube were classified using the autoregressive (AR) algorithm. The results were demonstrated using a dendrogram. The preliminary research revealed the natural arrangement of stress wave signals were grouped into two clusters. The stress wave signals from the healthy tube were grouped together in one cluster and the signals from the defective tubes were classified in another cluster. This approach was effective in separating different stress wave signals and allowed quicker and easier defect identification and interpretation in steel tubes.

• 소성∙가공
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• 제21권6호
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• pp.360-365
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• 2012

The aim of this study was to investigate the effects of forming depth on the deformation behavior of cup-like tubes made of AISI1020 steel in tube spinning process. Spinning process was performed on cup-like tubes, which had an inner diameter of 34mm and thicknesses of 7, 8.5 or 11.5mm. The forming depths achieved were 3, 4, and 5.5mm. The complex deformation behaviors occurring during the tube spinning process was explained using the experimental results. Also analyzed were the causes of the material buildup and the bulge defect of inner surface, observed on cross section of tubes. The relationship between tube spinning conditions and the height of bulge defect was examined. The results indicate that bulge defect is increased with a decrease of the forming depth. Moreover, a critical forming depth exists for preventing the generation of the bulge defect in the tube spinning process. The present results will be useful for future decisions of forming depths for successful tube spinning of cup-like tubes.

• 전기학회논문지
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• 제57권11호
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• pp.2095-2101
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• 2008

In this paper, we consider a performance improvement of neural network for predicting defect size of steam generator tube using early stopping. Usually, neural network is trained until MSE becomes less than a prescribed error goal. The smaller the error goal, the greater the prediction performance for the trained data. However, as the error goal is decreased, an over fitting is likely to start during supervised training of a neural network, which usually deteriorates the generalization performance. We propose that, for the prediction of an axisymmetric defect size, early stopping can be used to avoid the over-fitting. Through various experiments on the axisymmetric defect samples, we found that the difference bet ween the prediction error of neural network based on early stopping and that of ideal neural network is reasonably small. This indicates that the error goal used for neural network training for the prediction of defect size can be efficiently selected by early stopping.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2004년도 하계학술대회 논문집 B
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• pp.887-889
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• 2004

This paper presents an analysis of RPC probe signal in steam generator tube with defect using finite element method. Impedance signal is calculated according to the depth variation of defect in tube and change of frequency in same defect. As the depth of the defect and the operating frequency is increased, the magnitude of the signal is increased. From the result of this paper, we can obtain the information by the effect of defect and frequency.

• 대한기계학회논문집A
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• 제28권12호
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• pp.2004-2011
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• 2004

The applicability of the ultrasonic C-scan inspection is restricted due to the deterioration of mechanical properties of specimen during the test. Therefore, the aim of this research is applied to Eddy Current (EC) test substitute for the C-scan inspection in CFRP tube containing defects. This research is to evaluate the EC signals for the inspection of CFRP tube containing various circular hole defects (20% to 100% depth to the specimen thickness) using the unloading specimen and radial loading specimen. This study was considered the following points; 1) Analysis of EC signals for the inspection of saw-cut defect and circular hole defect, 2) The evaluation of defect depths and EC signals relationship. 3) Variation of EC signal owing to the radial load. In conclusions, the high frequency such as 300∼500 kHz made it possible to the inspection of 40% to 100% defects. Particularly, in case of 20% defect, the EC signal was not detected due to the noise of micro-crack and delamination. While the depth of the hole defects were decreasing, the difference of the phase angle between unloading specimen and radial loading specimen was gradually increasing.

• Archives of Plastic Surgery
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• 제33권2호
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• pp.213-218
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• 2006

As the large defect of peripheral nerve occurs, the autologous nerve graft is the most ideal method but it has many limitations due to donor site morbidities. Various materials have been developed for the nerve defect as the conduits, but none of these materials is satisfactory. Among them, $Gore-Tex^{(R)}$ tube seems to be one of the most ideal nerve conduit materials at peripheral nerve defect. Many researches have focused on finding the neurotrophic factors. It is recently demonstrated that Valproic acid(VPA) has an effect of axonal regeneration as a neurotrophic factor without enzymatic degradation and toxicity problems. The purpose of this study is to evaluate the effect of VPA on the nerve regeneration at the peripheral nerve defect. A 10 mm gap of rat sciatic nerve was made and $Gore-Tex^{(R)}$ tube filled with biceps femoris muscle was placed at the nerve defect site. We let the rat take VPA as drinking water in experimental group and did not give VPA to the control group. We estimated the results as electrophysiologic and histological aspects for 16 weeks after the surgery. The nerve conduction velocity, total myelinated axon count, myelin sheath thickness and mean nerve fiber diameter significantly increased in VPA-treated experimental group when compared to the control (p < 0.05). From the above results, we conclude that VPA promotes the nerve regeneration at the peripheral nerve defect site. It is suggested that $Gore-Tex^{(R)}$ tube filled with skeletal muscle and VPA administration may be a good substitute for autologous nerve graft.

• 한국정밀공학회:학술대회논문집
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• 한국정밀공학회 2007년도 추계학술대회 논문집
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• pp.359-360
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• 2007

• 한국정밀공학회:학술대회논문집
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• 한국정밀공학회 2009년도 춘계학술대회 논문집
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• pp.679-680
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• 2009

• 비파괴검사학회지
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• 제30권4호
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• pp.302-310
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• 2010

본 논문에서는 원자력 발전소 증기발생기 세관에 발생할 수 있는 결함의 크기측정에 사용되는 Bagging 신경회로망에 대한 연구를 수행하였다. Bagging은 부트스트랩(bootstrap) 샘플링에 기반을 둔 추정기 앙상블을 생성하는 방법이다. 증기발생기 세관의 결함 크기측정을 위하여 다양한 폭과 깊이를 갖는 4가지 결함패턴의 eddy current testing 신호를 생성하였다. 그 다음, 단일 신경회로망(single neural network; SNN)과 Bagging 신경회로망(Bagging neural network; BNN)을 구성하여 각 결함의 폭과 깊이를 추정하였다. SNN과 BNN 추정성능은 최대오차를 이용해서 측정하였다. 실험결과, 결함 깊이 추정시의 SNN과 BNN 최대오차는 0.117mm와 0.089mm 이었다. 또한, 결함 폭 추정 시에는 SNN과 BNN 최대오차는 0.494mm와 0.306mm 이었다. 이러한 실험결과는 BNN 추정성능이 SNN 추정성능보다 우수하다는 것을 보여준다.