• Proceedings of the Korean Powder Metallurgy Institute Conference
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• 2006.09a
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• pp.633-634
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• 2006

The aim of present work is to link geometrical parameter of maximum area of structural defect $\sqrt{area}\;_{max}$ (proposed by Y. Murakami, 1983) with surface fatigue mechanisms. Determined relations allow making predictions of surface fatigue properties of cemented carbides (WC-Co hardmetal - H15 - 85wt% WC and 15wt %Co, TiC-based cermets - T60/8 - 60wt %TiC and Fe/8wt% Ni and T70/14 - 70wt %TiC and Fe/14wt% Ni) in conditions of rolling contact and impact cycling loading. Pores considered being equivalent to small defects. Three comparative defects conditions are distinguished: surface pore, just below free surface and interior pores. The Vickers hardness of binder (as main responsible for the fracture mechanism of hardmetal and cermets) assumed to be the basis of such assumption. The estimate of this prediction has been done by analyzing the pore sizes using the statistics of extremes. The lower bound of fatigue properties can be correctly predicted by considering the maximum occurring pore size.

• Journal of Korean Society for Quality Management
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• v.49 no.3
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• pp.421-431
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• 2021

Purpose: For improving outgoing quality, this study presents a novel sampling framework based on predictive analytics. Methods: The proposed framework is composed of three steps. The first step is the variable selection. The knowledge-based and data-driven approaches are employed to select important variables. The second step is the model learning. In this step, we consider the supervised classification methods, the anomaly detection methods, and the rule-based methods. The applying model is the third step. This step includes the all processes to be enabled on real-time prediction. Each prediction model classifies a product as a target sample or random sample. Thereafter intensive quality inspections are executed on the specified target samples. Results: The inspection data of three Samsung products (mobile, TV, refrigerator) are used to check functional defects in the product by utilizing the proposed method. The results demonstrate that using target sampling is more effective and efficient than random sampling. Conclusion: The results of this paper show that the proposed method can efficiently detect products that have the possibilities of user's defect in the lot. Additionally our study can guide practitioners on how to easily detect defective products using stratified sampling

• Composites Research
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• v.34 no.6
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• pp.337-344
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• 2021

In this study, the progressive failure behavior of the composite fan blade dovetail element under tensile loading is numerically investigated through finite element(FE) simulation. The accuracy of prediction by FE simulation is verified through tensile testing. The dovetail element is one of the joints for coupling the fan blade with the disk in a turbofan engine. The dovetail element is usually made of a metal material such as titanium, but the application of composite material is being studied for weight reduction reasons. However, manufacturing defects such as drop-off ply and resin pocket inevitably occur in realizing complex shapes of the fan blade made by composite materials. To investigate the effect of these manufacturing defects on the composite fan blade dovetail element, we performed numerical simulation with FE model to compare the prediction of the FE model and the tensile test results. At this time, the cohesive zone model is used to simulate the delamination behavior. Finally, we found that FE simulation results agree with test results when considering thermal residual stress and through-thickness compression enhancement effect.

• Transactions of Materials Processing
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• v.15 no.4 s.85
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• pp.289-294
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• 2006

To reduce automobile parts weight, TWB(tailored welded blank) forming is widely used in panel forming. But products used TWB forming process have many defect, wrinkle, crack and springback. So study of TWB forming process character is very important. In this study one of the current problems of TWB forming was analyzed, especially for the try-out process of inner door panel without frame. A comparison was made between actual measurements and prediction of forming analysis for formability and springback. Also a new analysis die model which have additional plane on die surface was proposed to correct result of forming analysis. This proposed method overcomes the difference for TWB forming result between try-out and analysis.

• The Korean Journal of Health Service Management
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• v.7 no.1
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• pp.1-9
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• 2013

This study intends to identify the characteristics of cancer in-patients and those of cancer patients who defected to other medical institutions based on the summary of hospital discharge information of a university hospital for the purpose of improving work efficiency and maximizing the number of patients. The study used data on cancer patients registered in the database of C University Hospital in Gyeonggi Province for a period of one year between January 1 and December 31. The analysis results suggest that the commonalities of the cancer patients who defected to other medical institutions include no specific job, old age, and hospitalization through emergency room. In conclusion, hospitals need to identify the characteristics of cancer patients classified as patients who are prone to defect and the defection factors through this prediction model.

• Proceedings of the Korea Information Processing Society Conference
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• 2021.05a
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• pp.459-462
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• 2021

본 연구는 반도체 제조 공정중 발생하는 센서 데이터를 활용하여 딥러닝기반으로 불량을 예측하는 모델을 제안한다. 반도체 공장에서는 FDC((Fault Detection and Classification)라는 불량을 예측하는 시스템이 있지만, 공정의 복잡도가 높고 센서의 종류가 많아 공정 관리자가 모든 센서의 기준을 설정 및 관리하는데 한계가 있다. 이를 해결하기 위해 공정 설비의 센서 데이터를 딥러닝을 활용하여 학습시켜 센서 기준정보로 임계치를 제공하고, 가공중 발생하는 센서 데이터가 입력되면 정상 여부를 판정하는 모델을 제안한다.

• Proceedings of the Korea Information Processing Society Conference
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• 2008.05a
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• pp.351-354
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• 2008

소프트웨어 프로젝트를 진행할 때, 소프트웨어 개발에 투입할 노력의 정확한 추정과 더불어 소프트웨어 생명주기 단계별 적정한 개발노력을 투입하는 것은 프로젝트 성공을 위해 필요한 요소 중 하나이다. 조직의 과거 데이터를 활용한 기존의 개발노력 분배 방식은 단계별로 발생되는 결함의 양에 따라 개발노력의 투입량 변동이 발생될 수 있다. 본 연구에서는 CMMI 조직 프로세스성과(Organization Process Performance) 프로세스 기반의 결함 예측을 이용한 개발노력 분배 성과모델을 제시하고, 제시한 성과모델의 예측값과 프로젝트 수행 결과 값의 비교를 통해서 제시한 성과모델의 유효성 및 결함과 개발노력 분배의 연관성에 대해서 검증 하고자 한다.

• Journal of the Society of Disaster Information
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• v.18 no.2
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• pp.364-373
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• 2022

Purpose: The purpose of this paper is to develope smoke detection using AI model for detecting the initial fire in underground utility tunnels using CCTV Method: To improve detection performance of smoke which is high irregular, a deep learning model for fire detection was trained to optimize smoke detection. Also, several approaches such as dataset cleansing and gradient exploding release were applied to enhance model, and compared with results of those. Result: Results show the proposed approaches can improve the model performance, and the final model has good prediction capability according to several indexes such as mAP. However, the final model has low false negative but high false positive capacities. Conclusion: The present model can apply to smoke detection in underground utility tunnel, fixing the defect by linking between the model and the utility tunnel control system.

• 제어로봇시스템학회:학술대회논문집
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• 2003.10a
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• pp.150-155
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• 2003

We discussed a quantitative structure-activity relationships (QSAR) technique on incomplete data set. We proposed a new solver that used 2 kinds of multi-layer neural networks. One is to compensate the defect data, and another is to evaluate the QSAR. The solver can predict the defects in model QSAR data. By using them, we get very high precision QSAR. It is 5-10 times higher than that of a traditional method. However, in case of anti-cancer Carboquone, the prediction is not so complete. It was about O(3) wrong than the model calculation. The predicted values would have rather large error. It is caused by noisy observations of Carboquone. However, if we used the uncertain predictions, new data are included in QSAR. If not, they were omitted. The effect would not be little. Therefore, we evaluated the QSAR. The results are contrary to the expectation, are not so wrong. We believe that the wrong effect is suppressed by including information of new data.

• Composites Research
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• v.31 no.5
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• pp.251-259
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• 2018

In this paper, statistical volume element modeling method was developed for multi-scale progressive failure analysis of fiber reinforced composite materials. Big-size statistical volume elements (BSVEs) was considered to minimize the size effect in the micro-scale, by including as many fibers as possible. For that purpose, a mesh cutting method is suggested and adapted into the fiber model generator that creates finite element domain rapidly. The fiber defect model was also developed based on the experimental distribution of the fiber strength. The size effects from the local load sharing (LLS) are evaluated by increasing the fiber inclusion in the micro-scale model. Finally, continuum damage mechanics (CDM) model to the fiber direction was extracted from numerical analysis on BSVEs. And it was compared with strength prediction from typical representative volume element (RVE) model.