• Journal of the Korean Society of Manufacturing Technology Engineers
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• v.6 no.2
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• pp.76-82
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• 1997

The discrimination of flaw shape and sizing is very important subject in the material evaluation for semiconductor and new materials. The aim of this paper is to investigate the spectrum analysis of artificial defect signal captured from steel ball embedded in the resin. The results show that it can be evaluated quantitatively the size of artificial defect, from the amplitude variation of same frequency if the probe with same diameter and focal length is used. Comparing with the amplitude variation of the high frequency component and low frequency component obtained from the distance of defect center position, it can be estimated steel ball and flat bottm defect.

• Journal of Mechanical Science and Technology
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• v.18 no.9
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• pp.1493-1499
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• 2004

This paper describes the speckle shearing interferometry, a non-destructive optical method, for quantitative estimation of void defect and monitoring separation defect inside of a pneumatic tire. Previous shearing interferometry has not supplied quantitative result of inside defect, due to effective factors. In the study, factors related to the details of an inside defect are classified and optimized with pipeline simulator. The size and the shape of defect can be estimated accurately to find a critical point and also is closely related with shearing direction. The technique is applied for quantitative estimation of defects inside of a pneumatic tire. The actual traveling tire is monitored to reveal the cause of separation and the starting points. And also unknown void defects on tread are inspected and the size and shape of defects are estimated which has good agreement with the result of visual inspection.

• Structural Engineering and Mechanics
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• v.60 no.4
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• pp.667-691
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• 2016

This study focuses on the damage detection of defect types in plate structures based on wavelet transform (WT) and curvelet transform (CT). In particular, for damage detection of structures these transforms have been developed since the last few years. In recent years, the CT approach has been also introduced in an attempt to overcome inherent limitations of traditional multi-scale representations such as wavelets. In this study, the performance of CT is compared with WT in order to demonstrate the capability of WT and CT in detection of defect types in plate structures. To achieve this purpose, the damage detection of defect types through defect shape in rectangular plate is investigated. By using the first mode shape of plate structure and the distribution of the coefficients of the transforms, the damage existence, the defect location and the approximate shape of defect are detected. Moreover, the accuracy and performance generality of the transforms are verified through using experimental modal data of a plate.

• Nuclear Engineering and Technology
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• v.53 no.11
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• pp.3852-3859
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• 2021

This paper presents experimental and numerical analysis results regarding the effects of an incomplete penetration defect on the fatigue lives of socket welded pipes. For the experiment, four-point bending fatigue tests with various defect geometries (defect depth and circumferential length) were performed, and test results are presented in terms of stress-life data. The results showed that for circumferentially short defects, the fatigue life tends to increase with increasing crack depth, but for longer defects, the trend becomes the opposite. Finite element analysis showed that for short defects, the maximum principal stress decreases with increases in crack depth. For a longer defect, the opposite trend was found. Furthermore, the maximum principal stress tends to increase with an increase in defect length regardless of the defect depth.

• Transactions of the Korean Society of Automotive Engineers
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• v.6 no.1
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• pp.134-142
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• 1998

The effects of small hole defect size and effective case depth(ECD) on the four point bending fatigue limit of induction surface hardened S45C steel were investigated the fatigue limit evaluation of hardened materials is very difficult because of relations of the hardness gradient and residual stress. In this study, it was possible to characterize fatigue limit and fatigue life of induction surface hardened S45C steel in terms of the hole defect size and effective case depth(ECD) and quantitative evaluation of the fatigue limit with hole defects use Murakami's evaluation method and the range of evaluated values is a good accuracy compared with results.

• Proceedings of the Korean Institute of Electrical and Electronic Material Engineers Conference
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• 2001.07a
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• pp.578-581
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• 2001

Silicon wafers using DRAM devices required for high cleaning technology and this cleaning technology was evaluated by defect level or electron life time. This paper examined the correlation of SPV(Surface Photo Voltaic Analyzer) which analyzes diffusion length of minority carriers and DLTS(Deep level Transient Spectroscope) which analyzes defect level.

• Journal of the Korea Institute of Building Construction
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• v.7 no.4
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• pp.125-132
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• 2007

The use of Self-Leveling material is increasing recently. This paper assesses the quality of surface layer of concrete floor when Self-Leveling material is defective. The paper shows how to predict the defect of SL material before construction begins. The relationship between the quality of surface layer of concrete floor and the defect of SL material was determined and the quality of surface layer of concrete floor was then estimated. The relations between the quality of surface layer and the defect of SL material were determine considering surface strength, moisture, and consistency of surface layer. Absorbing amount was used as the indicator of consistency and the absorbing amount of test material was measured. Then the relations between the test material and surface strength were determined. Generally concrete floor with greater consistency has greater surface strength, however in this study, we hound that high impact concrete floor could have lower surface strength as the consistency gets bigger. The relations between the level of defect occurred in SL material and the quality of surface layer were examined and we clarified that the surface layer with lower consistency gets higher possibility to occur exfoliation in early stage, one or two weeks after constructing SL material. When the consistency is sufficient, the occurring situation of defect depends upon the moisture of surface layer. Little amount of moisture gets higher possibility not to occur the defect. As the amount increases, fissure generates and early exfoliation may occur. In addition, the level of fissure is highly related with the surface strength.

• Journal of Veterinary Clinics
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• v.33 no.6
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• pp.332-339
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• 2016

Polycaprolactone (PCL) scaffold have been developed as an alternative to natural donor tissue to repair a large osteochondral defect. The objective of this study is to evaluate efficacy and biocompatibility of bilayer PCL scaffold implanted for osteochondral repair in rabbit. Twenty-two male New Zealand White rabbits were used in this animal experiment. Rabbits were divided into three groups. Experimental surgery was carried out under general anesthesia. Osteochondral defects (5 mm diameter and 5 mm deep) were made in the center of the patellar groove using a 5 mm diameter biopsy punch. In group I (3D plotting) and group II (salt-leaching), the scaffold was implanted using the press-fitted technique into the defect. In control group, after osteochondral defect was created, the defect was left without implant. After four and eight weeks, rabbits were sacrificed and the defects were evaluated by macro -and microscopical methods. There were not found animal death and severe inflammatory evidence during the experimental periods. There were no significant differences between the experimental groups in gross evaluation. However the group I scored significantly higher than group II at 8 weeks in histological evaluation (P < 0.05). The 3-D plotting PCL scaffold was more suitable method for reconstruction of osteochondral defect than a salt-leaching PCL scaffold.

• Journal of the Korean Society for Railway
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• v.10 no.1 s.38
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• pp.1-6
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• 2007

Railway wheels in service are regularly checked by ultrasonic testing, acoustic emission and eddy current testing method and so on. However, ultrasonic testing is sometimes inadequate for sensitively detecting the cracks in railway wheel which is mainly because of the fact of crack closure. Recently, many researchers have actively fried to improve precision for defect detection of railway wheel. The development of a nondestructive measurement tool for wheel defects and its use for the maintenance of railway wheels would be useful to prevent wheel failure. The induced current focusing potential drop(ICFPD) technique is a new non-destructive tasting technique that can detect defects in railway wheels by applying on electro-magnetic field and potential drops variation. In the present paper, the ICFPD technique is applied to the detection of surface and internal defects for railway wheels. To defect the defects for railway wheels, the sensor for ICFPD is optimized and the tests are carried out with respect to 4 surface defects and 6 internal defects each other. The results show that the surface crack depth of 0.5 mm and internal crack depth of 0.7 mm in wheel tread could be detected by using this method. The ICFPB method is useful to detect the defect that initiated in the tread of railway wheels

• Transactions of Materials Processing
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• v.31 no.4
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• pp.229-239
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• 2022

Convolution Neural Network(CNN) is a class of deep learning algorithms and can be used for image analysis. In particular, it has excellent performance in finding the pattern of images. Therefore, CNN is commonly applied for recognizing, learning and classifying images. In this study, the surface defect classification performance of Al 6061 extruded material using CNN-based algorithms were compared and evaluated. First, the data collection criteria were suggested and a total of 2,024 datasets were prepared. And they were randomly classified into 1,417 learning data and 607 evaluation data. After that, the size and quality of the training data set were improved using data augmentation techniques to increase the performance of deep learning. The CNN-based algorithms used in this study were VGGNet-16, VGGNet-19, ResNet-50 and DenseNet-121. The evaluation of the defect classification performance was made by comparing the accuracy, loss, and learning speed using verification data. The DenseNet-121 algorithm showed better performance than other algorithms with an accuracy of 99.13% and a loss value of 0.037. This was due to the structural characteristics of the DenseNet model, and the information loss was reduced by acquiring information from all previous layers for image identification in this algorithm. Based on the above results, the possibility of machine vision application of CNN-based model for the surface defect classification of Al extruded materials was also discussed.