• Clean Technology
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• v.23 no.1
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• pp.90-94
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• 2017

These days much efforts are being dedicated to wind power as a potential source of renewable energy. To maintain effective and uniform generation of energy, defect preservation of turbine blade is essential because it directly takes effects on the efficiency of power generation. For the effective maintenance, early measurements of blade defects are very important. However, current technologies such as ultrasonic waves and thermal imaging inspection methods are not suitable because of long inspection time and non-real time inspection. To supplement the problems, the study introduced a method for real time defect monitoring of a blade surface based on surface wave technology. We examined the effect of various parameters such as micro-cracks and peelings on the propagation of surface wave.

• Korean Journal of Construction Engineering and Management
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• v.16 no.2
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• pp.21-28
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• 2015

Construction defects are major components that result in cost overruns and schedule delays in construction projects. There have been extensive research efforts to investigate the cause of defects. However, little effort has been devoted to analyze correlation among various reasons of construction defects while a defect is not usually an outcome of a single cause, but rather occurs when several interrelated causes combine. Based on this recognition, this paper analyzes 831 nonconformity reports collected from 30 construction projects in Korea from 2011 to 2014. The correlation analysis revealed that a significant portion of construction defects occurred in the procurement and construction phase and as the pattern of function defect and installation defect. Triggered by human error, defective material and faulty method, these defects are treated by conccession, repair, rework that can significantly lower the cost and schedule performance. This paper is significant in terms of providing a theoretical basis for analyzing correlation among various reasons of construction defects and quantitative measures for establishing effective defect prevention strategies.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.13 no.1
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• pp.55-62
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• 2013

Automatic inspection system is required for increment of copper plate production and demand expansion. Thus 3D surface form and defect detection of copper plate calls for 3D image and GUI analysis. Limitation of 2D analysis, such as error occurrence and decision difficulty makes eye inspection automatic. Automatic inspection is able to raise accurate inspection rate and productivity efficiency elevation. In this paper defect classification is defined and inspection system is implemented. Defect analysis algorithms and GUI for 3D image analysis is developed and tested.

• Journal of the Korean Society for Nondestructive Testing
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• v.27 no.3
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• pp.246-254
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• 2007

The scanning laser source (SLS) technique is a promising new laser ultrasonic tool for the detection of small surface-breaking defects. The SLS approach is based on monitoring the changes in laser-generated ultrasound as a laser source is scanned over a defect. Changes in amplitude and frequency content are observed for ultrasound generated by the laser over uniform and defective areas. The SLS technique uses a point or a short line-focused high-power laser beam which is swept across the test specimen surface and passes over surface-breaking or subsurface flaws. The ultrasonic signal that arrives at the Rayleigh wave speed is monitored as the SLS is scanned. It is found that the amplitude and frequency of the measured ultrasonic signal have specific variations when the laser source approaches, passes over and moves behind the defect. In this paper, the setup for SLS experiments with full B-scan capability is described and SLS signatures from small surface-breaking and subsurface flaws are discussed using a point or short line focused laser source.

• Journal of the Korean Society of Manufacturing Process Engineers
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• v.19 no.5
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• pp.82-90
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• 2020

Due to the complicated shape of the spider, the production method was changed from cold to warm forging. Finite element analysis was performed to predict the forging load and shape using the enclosed hydraulic die set. As the forging load increases due to the spider die volume, die stress analyses were performed to optimize the die design in order to reduce the die stress in various conditions. Large deformation while producing the complicated forging parts induces high forging load, which is one of the main parameters of the forging surface defects. The forging process was analyzed to find out the root cause of the surface defects generated during the spider production for various parameters, thereby revealing that the radius of die in the defect zone influenced the air trap depth, being the root cause of the surface defect. It was verified that die life was increased and the surface defect was eliminated by changing the die design during the mass production test.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2019.05a
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• pp.442-444
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• 2019

Fabric defect classification is an important issue in fabric quality control. However, automated classification is difficult because it is hard to identify various types of defects in images. classification of fabric defects mostly rely on human ability. In this paper, to solve this problem we apply Convolutional Neural Networks (CNN) for fabric defect classification. To make training CNN easier, we propose a method that is manually attentive defect regions in images. we compare the proposed method with the original image and confirm that the proposed method is effective for learning.

• 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.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2021.10a
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• pp.290-291
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• 2021

Defects in the coating process of plastic automotive components are caused by various causes and phenomena. The correlation between defect occurrence rate and meteorological and environmental conditions such as temperature, humidity, and fine dust was analyzed. The defect rate data categorized by type and cause was collected for a year from a automotive parts coating company. This data and its correlation with environmental condition was acquired and experimented by machine learning techniques to predict the defect rate at a certain environmental condition. Correspondingly, the model predicted 98% from fine dust and 90% from curtaining (runs, sags) and hence proved its reliability.

• Journal of the Korean Association of Oral and Maxillofacial Surgeons
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• v.45 no.2
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• pp.97-107
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• 2019

Objectives: Small animal maxillofacial models, such as non-segmental critical size defects (CSDs) in the rabbit mandible, need to be standardized for use as preclinical models of bone regeneration to mimic clinical conditions such as maxillofacial trauma. The objective of this study is the establishment of a mechanically competent CSD model in the rabbit mandible to allow standardized evaluation of bone regeneration therapies. Materials and Methods: Three sizes of bony defect were generated in the mandibular body of rabbit hemi-mandibles: $12mm{\times}5mm$, $12mm{\times}8mm$, and $15mm{\times}10mm$. The hemi-mandibles were tested to failure in 3-point flexure. The $12mm{\times}5mm$ defect was then chosen for the defect size created in the mandibles of 26 rabbits with or without cautery of the defect margins and bone regeneration was assessed after 6 and 12 weeks. Regenerated bone density and volume were evaluated using radiography, micro-computed tomography, and histology. Results: Flexural strength of the $12mm{\times}5mm$ defect was similar to its contralateral; whereas the $12mm{\times}8mm$ and $15mm{\times}10mm$ groups carried significantly less load than their respective contralaterals (P<0.05). This demonstrated that the $12mm{\times}5mm$ defect did not significantly compromise mandibular mechanical integrity. Significantly less (P<0.05) bone was regenerated at 6 weeks in cauterized defect margins compared to controls without cautery. After 12 weeks, the bone volume of the group with cautery increased to that of the control without cautery after 6 weeks. Conclusion: An empty defect size of $12mm{\times}5mm$ in the rabbit mandibular model maintains sufficient mechanical stability to not require additional stabilization. However, this defect size allows for bone regeneration across the defect. Cautery of the defect only delays regeneration by 6 weeks suggesting that the performance of bone graft materials in mandibular defects of this size should be considered with caution.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 1997.04a
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• pp.123-127
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• 1997

Recently, it is gradually raised necessity that interface is measured accurately and managed in industrial circles and medical world, An Ultrasonic wave transmitted from a focused beam transducer is being expected as a powerful tool for NDE of micro-defect. The ultrasonic NDE of the defect is based on the form of the wave reflected form the interface In this study, regarding to the thickness of film which is in opaque object and thickness measurement was done by MEM-cepstrum analysis of received ultrasonic wave. In measument results, film thickness which is beyond distance resolution capacity was measured accurately. Also, automatically repeated discrimination analysis method can be decided in the category of all kinds of defects on semiconductor package.