• Proceedings of the KSME Conference
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• 2001.06c
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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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• v.19 no.1
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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.

• Transactions of Materials Processing
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• v.21 no.6
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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.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.57 no.11
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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.

• Proceedings of the KIEE Conference
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• summer
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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.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.28 no.12
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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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• v.33 no.2
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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.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2007.11a
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• pp.359-360
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• 2007

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2009.06a
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• pp.679-680
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• 2009

• Journal of the Korean Society for Nondestructive Testing
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• v.30 no.4
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• pp.302-310
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• 2010

In this paper, we studied Bagging neural network for predicting defect size of steam generator(SG) tube in nuclear power plant. Bagging is a method for creating an ensemble of estimator based on bootstrap sampling. For predicting defect size of SG tube, we first generated eddy current testing signals for 4 defect patterns of SG tube with various widths and depths. Then, we constructed single neural network(SNN) and Bagging neural network(BNN) to estimate width and depth of each defect. The estimation performance of SNN and BNN were measured by means of peak error. According to our experiment result, average peak error of SNN and BNN for estimating defect depth were 0.117 and 0.089mm, respectively. Also, in the case of estimating defect width, average peak error of SNN and BNN were 0.494 and 0.306mm, respectively. This shows that the estimation performance of BNN is superior to that of SNN.