• 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.

• 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.

• Proceedings of the Korean Society for Technology of Plasticity Conference
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• 2008.10a
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• pp.333-336
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• 2008

The groove rolling is a process that transforms the bloom or billet into a shape with circular section through a series of rolling. Inhibition of surface defect generation in groove rolling is a matter of great importance and therefore many research groups proposed a lot of models to find the location of surface defect initiation. In this study, we propose a model for maximum shear stress ratio over equivalent strain to catch the location of surface defect onset. This model is coupled with element removing method and applied to box groove rolling of POSCO No. 3 Rod Mill. Results show that proposed model in this study can find the location of surface defect initiation during groove rolling when finite element analysis results is compared with experiments. The proposed criterion has been applied successfully to design roll grooves which inhibits the generation of surface defect.

• Transactions of Materials Processing
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• v.17 no.8
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• pp.607-612
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• 2008

The groove rolling is a process that transforms the bloom or billet into a shape with circular section through a series of rolling. Inhibition of surface defect generation in groove rolling is a matter of great importance and therefore many research groups proposed a lot of models to find the location of surface defect initiation. In this study, we propose a model for maximum shear stress ratio over equivalent strain to catch the location of surface defect onset. This model is coupled with element removing method and applied to box groove rolling of POSCO No.3 Rod Mill. Results show that proposed model in this study can find the location of surface defect initiation during groove rolling when finite element analysis results is compared with experiments. The proposed criterion has been applied successfully to design roll grooves which inhibit the generation of surface defect.

• 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 the Semiconductor & Display Technology
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• v.21 no.2
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• pp.68-73
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• 2022

In this study, we produced the surface defect detection model to automatically detect defect bone plates using a deep learning algorithm. Bone plates with a width and a length of 50 mm are most used for fracture treatment. Normal bone plates and defective bone plates were printed on the 3d printer. Normal bone plates and defective bone plates were photographed with 1,080 pixels using the webcam. The total quantity of collected images was 500. 300 images were used to learn the defect detection model. 200 images were used to test the defect detection model. The mAP(Mean Average Precision) method was used to evaluate the performance of the surface defect detection model. As the result of confirming the performance of the surface defect detection model, the detection accuracy was 96.3 %.

• Journal of the Semiconductor & Display Technology
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• v.19 no.3
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• pp.1-6
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• 2020

In this paper, a method for improving the defect classification performance in steel plate surface has been studied, based on DWT(discrete wavelet transform) and SVM(support vector machine). Surface images of the steel plate have low contrast, uneven, and featureless, so that the contrast between defect and defect-free regions is not discriminated. These characteristics make it difficult to extract the feature of the surface defect image. In order to improve the characteristics of these images, a synthetic images based on discrete wavelet transform are modeled. Using the synthetic images, edge-based features are extracted and also geometrical features are computed. SVM was configured in order to classify defect images using extracted features. As results of the experiment, the support vector machine based classifier showed good classification performance of 94.3%. The proposed classifier is expected to contribute to the key element of inspection process in smart factory.

• Journal of Institute of Control, Robotics and Systems
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• v.22 no.4
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• pp.247-252
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• 2016

In this paper, we propose a new defect detection technology for micro-defect on the surface of steel products. Due to depth and size of microscopic defect, slop of surface and vibration of strip, the conventional optical method cannot guarantee the detection performance. To solve the above-mentioned problems and increase signal to noise ratio, a novel retro-schlieren method that consists of retro reflector and knife edge is proposed. Moreover dual switching lighting method is also applied to distinguish uneven micro defects and surface noise. In proposed method, defective regions are represented by a black and white pattern. This pattern is detected by a defect detection algorithm with Gabor filter. Experimental results by simulator for sample defects of cold rolled steel show that the proposed method is effective.

• Journal of the Semiconductor & Display Technology
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• v.20 no.1
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• pp.64-69
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• 2021

In this paper, a method for improving the defect classification performance in low contrast, ununiformity and featureless steel plate surfaces has been studied based on deep convolution neural network and transfer-learning neural network. The steel plate surface images have low contrast, ununiformity, and featureless, so that the contrast between defect and defect-free regions are not discriminated. These characteristics make it difficult to extract the feature of the surface defect image. A classifier based on a deep convolution neural network is constructed to extract features automatically for effective classification of images with these characteristics. As results of the experiment, AlexNet-based transfer-learning classifier showed excellent classification performance of 99.43% with less than 160 seconds of training time. The proposed classification system showed excellent classification performance for low contrast, ununiformity, and featureless surface images.

• Journal of Ocean Engineering and Technology
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• v.3 no.2
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• pp.601-601
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• 1989

The behavior of fatigue was studied by using low carbon steel bar with microhole(artificial small defect) under the condition of rotary bending stress which is made artificially at smooth surface and round notch root. The results obtained can be summerized; The behavior of non-propagating cracks which are produced at both tips of small defect occurred to dissymmetry, and it is found to be double size of small defect. For the range of l>lc, threshold stress intensity is constant. However, for the range of l