• Journal of Information Processing Systems
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• v.19 no.1
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• pp.109-117
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• 2023

Defect detection is one of the key factors in fabric quality control. To improve the speed and accuracy of denim fabric defect detection, this paper proposes a defect detection algorithm based on cascading feature extraction architecture. Firstly, this paper extracts these weight parameters of the pre-trained VGG16 model on the large dataset ImageNet and uses its portability to train the defect detection classifier and the defect recognition classifier respectively. Secondly, retraining and adjusting partial weight parameters of the convolution layer were retrained and adjusted from of these two training models on the high-definition fabric defect dataset. The last step is merging these two models to get the defect detection algorithm based on cascading architecture. Then there are two comparative experiments between this improved defect detection algorithm and other feature extraction methods, such as VGG16, ResNet-50, and Xception. The results of experiments show that the defect detection accuracy of this defect detection algorithm can reach 94.3% and the speed is also increased by 1-3 percentage points.

• Journal of Korea Multimedia Society
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• v.23 no.5
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• pp.633-640
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• 2020

Defect region segmentation of TFT-LCD images is performed by combining defect pixels detected by a defect detection method into defect region, or by using morphological operations to segment defect region. Therefore, the result of segmentation of the defect region is highly dependent on the defect detection result. In this paper, we propose a method which segments defect regions sequentially according to the possibility of being included in defect regions in TFT-LCD images. The proposed method repeats the process of detecting a seed using the median value and the median absolute deviation of the image, and segments the defect region using the seeded region growing method. We confirmed the superiority of the proposed method to segment defect regions using pseudo-images and real TFT-LCD images.

• Journal of Energy Engineering
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• v.24 no.4
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• pp.55-62
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• 2015

It is expected that the size of welding defect affects the mechanical performance of welded medium density polyethylene (MDPE) pipe joints. In this study, butt fusion welded MDPE pipe joints with a single spherical or planar defect of various sizes were studied using experimental crush testing and also by finite element method. The crush test showed that the mechanical performance of crush was not affected by the size and geometry of a single welding defect when the defect size was increased to 45% of the pipe's wall thickness. The simulation results indicated that the effect of the single welding defect on the Von Mises stress distribution near the defect explained the reason of the test results.

• Transactions of Materials Processing
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• v.23 no.3
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• pp.159-163
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• 2014

In the current study, the effect of small surface defects in the starting material including roughness, indentations, or scratches, which are perpendicular to the direction of drawing, on the product quality is investigated using the finite element method. An axisymmetric defect is assumed. Such defects are defined by a cylindrical defect area and two tapered regions connecting the defect area to the non-defective area of the material. Various conditions for these initial surface defects are considered, including defect depth, defect slope and defect length. To describe the plastic deformation of the defect in detail during the simulation, local remeshing is applied. Based on the finite element results, defect disappearance maps were generated. It was found that defect disappearance is significantly dependent on the defect depth and the defect length coupled with the defect slope.

• 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.

• Journal of the Semiconductor & Display Technology
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• v.20 no.2
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• pp.109-114
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• 2021

When we inspect scaffold defect using sight, inspecting performance is decrease and inspecting time is increase. We need for automatically scaffold defect detection method to increase detection accuracy and reduce detection times. In this paper. We produced scaffold defect classification models using densenet, alexnet, vggnet algorithms based on CNN. We photographed scaffold using multi dimension camera. We learned scaffold defect classification model using photographed scaffold images. We evaluated the scaffold defect classification accuracy of each models. As result of evaluation, the defect classification performance using densenet algorithm was at 99.1%. The defect classification performance using VGGnet algorithm was at 98.3%. The defect classification performance using Alexnet algorithm was at 96.8%. We were able to quantitatively compare defect classification performance of three type algorithms based on CNN.

• Journal of the Semiconductor & Display Technology
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• v.20 no.3
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• pp.125-130
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• 2021

Scaffold is used to produce bio sensor. Scaffold is required high dimensional accuracy. 3D printer is used to manufacture scaffold. 3D printer can't detect defect during printing. Defect detection is very important in scaffold printing. Real-time defect detection is very necessary on industry. In this paper, we proposed the method for real-time scaffold defect detection. Real-time defect detection model is produced using CNN(Convolution Neural Network) algorithm. Performance of the proposed model has been verified through evaluation. Real-time defect detection system are manufactured on hardware. Experiments were conducted to detect scaffold defects in real-time. As result of verification, the defect detection system detected scaffold defect well in real-time.

• International conference on construction engineering and project management
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• 2015.10a
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• pp.232-235
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• 2015

Defect data contains experiential knowledge about specific work conditions. And the number of projects performed by a company is too limited for an individual to experience the various defects under the current complex construction environment. Therefore, in order to manage and prevent a reoccurrence of defects, a proper data feedback mechanism is required. However, most defect data are stored in unstructured ways, resulting in the fundamental problem of data utilization. In this paper, a new framework is proposed by using linked data technologies to improve defect data utilization. The target of this framework is to convert defect data to the ontology-based linked data format for sharing defect data from different data sources. To demonstrate it, some technical solutions are implemented by using real cases. The proposed approach can reduce data search time and improve the accuracy of search results as well. Moreover, the proposed approach can be applied to other domains that need to refer to external sources such as safety, specification, product, and regulation.

• Transactions on Electrical and Electronic Materials
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• v.5 no.4
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• pp.143-147
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• 2004

Surface defects and bulk defects on SOI wafers are studied. Two new metrologies have been proposed to characterize surface and bulk defects in epitaxial layer transfer (ELTRAN) wafers. They included the following: i) laser scattering particle counter and coordinated atomic force microscopy (AFM) and Cu-decoration for defect isolation and ii) cross-sectional transmission electron microscope (TEM) foil preparation using focused ion beam (FIB) and TEM investigation for defect morphology observation. The size of defect is 7.29 urn by AFM analysis, the density of defect is 0.36 /cm$^2$ at as-direct surface oxide defect (DSOD), 2.52 /cm$^2$ at ox-DSOD. A hole was formed locally without either the silicon or the buried oxide layer (Square Defect) in surface defect. Most of surface defects in ELTRAN wafers originate from particle on the porous silicon.