• Proceedings of the Korean Society of Precision Engineering Conference
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• 2010.05a
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• pp.831-832
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• 2010

• Journal of the Korean Society for Precision Engineering
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• v.24 no.4 s.193
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• pp.7-14
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• 2007

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2011.06a
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• pp.1149-1150
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• 2011

• Journal of Mechanical Science and Technology
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• v.14 no.5
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• pp.530-536
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• 2000

In this paper, the influence of several factors such as hardness, internal defect and non-propagating crack on fatigue limits was investigated with three kinds of ductile iron specimens. From the experimental results the fatigue limits were examined in relation with hardness and tensile strength in case of high strength specimens under austempering treatment; in consequence the marked improvement of fatigue limits were not showed. The maximum defect size was an important factor to predict and to evaluate the fatigue limits of ductile irons. And, the quantitative relationship between the fatigue limits$({\sigma}_w)$ and the maximum defect sizes $(\sqrt{area}_{max})$ was expressed as ${\sigma}_w^n{\cdot}{\sqrt{area}}_{max}=C_2$. Also, it was possible to explain the difference for the fatigue limits in three ductile irons by introduction of the non-propagating crack rates.

• Journal of the Semiconductor & Display Technology
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• v.19 no.3
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• pp.77-81
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• 2020

In this paper, we create scaffold defect classification models using machine learning based data. We extract the characteristic from collected scaffold external images using USB camera. SVM, KNN, MLP algorithm of machine learning was using extracted features. Classification models of three type learned using train dataset. We created scaffold defect classification models using test dataset. We quantified the performance of defect classification models. We have confirmed that the SVM accuracy is 95%. So the best performance model is using SVM.

• Journal of the Semiconductor & Display Technology
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• v.12 no.2
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• pp.57-61
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• 2013

This paper presents adaptive neuro-fuzzy inference based defect detection method for various defect types, such as micro-crack, fingerprint and contamination, in heterogeneously textured surface of polycrystalline solar wafers. Polycrystalline solar wafer consists of various crystals so the surface of solar wafer shows heterogeneously textures. Because of this property the visual inspection of defects is very difficult. In the proposed method, we use local binary feature and fuzzy reasoning for defect detection. Experimental results show that our proposed method achieves a detection rate of 80%~100%, a missing rate of 0%~20% and an over detection (overkill) rate of 9%~21%.

• Journal of the Semiconductor & Display Technology
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• v.19 no.2
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• pp.26-30
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• 2020

In this paper, we studied the problem if the experiment number occurring in order to identify defect in scaffold. We need to change each of the 5 print factor to predict defect when printing disk type scaffold using FDM 3d printer. So then the number of scaffold print will be more than 100,000 times. This experiment number is difficult to perform in the field. In order to solve this problem, we have produced a prediction model based on machine learning multiple linear regression using print conditions and defect scaffold data for print conditions. The prediction model produced was verified through experiments. The verification confirmed that the error was less than 0.5 %. We have confirmed that satisfied within the target margin of error 5 %.

• Journal of Electrical Engineering and Technology
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• v.9 no.4
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• pp.1360-1364
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• 2014

In this paper, RCS characteristics on defect pattern of crossed dipole slot FSS having a finite size have been analyzed. To analyze RCS, we applied the electric field integral equation analysis which applies BiCGSTab algorithm with iterative method and uses RWG basis function. To verify the validity of this paper, RCS of PEC sphere has been compared to the theoretical results and FSSs with defect patterns are fabricated and measured. As defect patterns in FSS, missing one column, missing some elements, and discontinuity in surfaces are simulated and compared with the measurement results. Resonant frequency shifts in pass band and changes in bandwidth are observed. From the results, precisely predicting and designing frequency characteristics over defect patterns are essential when applying FSS structures such as FSS radomes.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1998.06a
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• pp.208-212
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• 1998

This paper suggests the method to recognize the various defect patterns of cold mill strip using binary decision tree constructed by genetic algorithm automatically. In case of classifying the complex the complex patterns with high similarity like the defect patterns of cold mill strip, the selection of the optimal feature set and the structure of recognizer is important for high recognition rate. In this paper genetic algorithm is used to select a subset of the suitable features at each node in binary decision tree. The feature subset of maximum fitness is chosen and the patterns are classified into two classes by linear decision function. After this process is repeated at each node until all the patterns are classified respectively into individual classes. In this way , binary decision tree classifier is constructed automatically. After construction binary decision tree, the final recognizer is accomplished by the learning process of neural network using a set of standard p tterns at each node. In this paper, binary decision tree classifier is applied to recognition of the defect patterns of cold mill strip and the experimental results are given to show the usefulness of the proposed scheme.

• Journal of Biosystems Engineering
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• v.32 no.5
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• pp.324-331
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• 2007

The purpose of this study is to analysis effects of defect factors of combine header for cutting speed of combine header, feeding depth of straw, and cylinder speed of thresher. Measurement system for defect factors was consists of sensors to monitor the combine operation and I/O interface to convert the signals. Cutting speed of combine header, feeding depth of straw, cylinder speed of thresher were measured and analyzed. The data were collected from three paddy field during rice harvesting. The tests were conducted at different grounding speeds, lug troubles, and cutter condition. The one way ANOVA and the multiple comparison tests were performed. The results showed that the measured data were useful to monitor the defect factors of combine during harvesting. The faults conditions of grounding speeds, lug troubles, and cutter conditions affected cutting speeds, feeding depths and cylinder speeds of the combine. The data seem to be useful to analysis the faults conditions of combine header.