• 한국공작기계학회:학술대회논문집
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• 한국공작기계학회 1996년도 춘계학술대회 논문집
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• pp.24-29
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• 1996

The seniconductor, which is precision product, requires many inspection processes. The surface conditions of the semiconductor chip effect on the functions of the semiconductors. The defects of the chip surface is crack or void. Because general inspection method requires many inspection processes, the inspection system which searches immediately and preciselythe defects of the semiconductor chip surface. We propose the inspection method by using the computer vision system. This study presents an image processing algorithm for inspecting the surface defects(crack, void)of the semiconductor test samples. The proposed image processing algorithm aims to reduce inspection time, and to analyze those experienced operator. This paper regards the chip surface as random texture, and deals with the image modeling of randon texture image for searching the surface defects. For texture modeling, we consider the relation of a pixel and neighborhood pixels as noncasul model and extract the statistical characteristics from the radom texture field by using the 2D AR model(Aut oregressive). This paper regards on image as the output of linear system, and considers the fidelity or intelligibility criteria for measuring the quality of an image or the performance of the processing techinque. This study utilizes the variance of prediction error which is computed by substituting the gary level of pixel of another texture field into the two dimensional AR(autoregressive model)model fitted to the texture field, estimate the parameter us-ing the PAA(parameter adaptation algorithm) and design the defect detection filter. Later, we next try to study the defect detection search algorithm.

• 대한기계학회논문집A
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• 제35권10호
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• pp.1243-1248
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• 2011

내부결함은 주조제품의 강도 및 피로 수명에 있어 상당한 영향을 미치기 때문에 주조공정에서 주요 관심사 이다. 일반적으로 내부결함은 응력집중을 발생시키며 균열의 시작점이 되므로 피로 수명과 같은 기계적 거동에 있어 수축공과 같은 결함을 이해하는 것이 중요하다. 본 논문에서는 내부결함을 고려한 인장시편에 대해 피로시험을 수행하고 주조결함을 고려할 때의 특정하중피로노치 계수를 산정하였다. 실제 내부결함은 산업용 CT 장비를 통해서 확인하였으며 확인된 결함은 형상단순화법에 의해 타원체로 단순화 하고 응력해석과 피로해석을 수행하였다. 그 결과 우리가 제안한 방법이 기계적 거동에 있어 내부결함의 영향을 조사하고 피로수명 등을 예측함에 있어 유용함을 확인할 수 있었다.

• 융합정보논문지
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• 제11권11호
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• pp.44-50
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• 2021

최근 빅데이터를 수용하기 위한 대용량 저장 장치가 필요한 엔터프라이즈 저장 시스템에서는 비용과 크기 대비 직접도가 높은 대용량의 플래시 메모리 기반 저장 장치를 많이 사용하고 있다. 본 논문에서는 엔터프라이즈 대용량 저장 장치의 신뢰도와 이용성에 직접적인 영향을 주는 플래시 메모리 미디어의 수명을 극대화 하기 위해 경사하강법을 적용한 고효율 수명 예측 방법을 제안한다. 이를 위해 본 논문에서는 불량 발생 빈도를 학습하기 위한 메타 데이터를 저장하는 매트릭스의 구조를 제안하고 메타데이터를 이용한 비용 모델을 제안한다. 또한 학습된 범위를 벗어난 불량이 발생 했을 때 예외 상황에서의 수명 예측 정책을 제안한다. 마지막으로 시뮬레이션을 통해 본 논문에서 제안하는 방법이 이전까지 플래시 메모리의 수명 예측을 위해 사용되어 온 고정 횟수 기반 수명 예측 방법과 예비 블록의 남은 비율을 기반으로 하는 수명 예측 방법 대비 수명을 극대화 할 수 있음을 증명하여 우수성을 확인했다.

• Nuclear Engineering and Technology
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• 제54권3호
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• pp.834-841
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• 2022

System thermal-hydraulic (STH) code is adopted for nuclear safety analysis. The critical flow model (CFM) is significant for the accuracy of STH simulation. To overcome the defects of current CFMs (low precision or long calculation time), a CFM based on a genetic neural network (GNN) has been developed in this work. To build a powerful model, besides the critical mass flux, the critical pressure and critical quality were also considered in this model, which was seldom considered before. Comparing with the traditional homogeneous equilibrium model (HEM) and the Moody model, the GNN model can predict the critical mass flux with a higher accuracy (approximately 80% of results are within the ±20% error limit); comparing with the Leung model and the Shannak model for critical pressure prediction, the GNN model achieved the best results (more than 80% prediction results within the ±20% error limit). For the critical quality, similar precision is achieved. The GNN-based CFM in this work is meaningful for the STH code CFM development.

• Structural Engineering and Mechanics
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• 제38권4호
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• pp.443-457
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• 2011

The loss of strength in a structure as a result of cyclic loads over a period of life time is an important phenomenon for the life-cycle analysis. Service loads are accentuated at the areas of stress concentration, mainly at the connection of components. Structural components unavoidably are affected by defects such as surface scratches, surface roughness and weld defects of random sizes, which usually occur during the manufacturing and handling process. These defects are shown to have an important effect on the fatigue life of the structural components by promoting crack initiation sites. The value of equivalent initial flaw size (EIFS) is calculated by using the back extrapolation technique and the Paris law of fatigue crack growth from results of fatigue tests. We try to analyze the effect of EIFS distribution in a multiple site damage (MSD) specimen by using the extended finite element method (XFEM). For the analysis, fatigue tests were conducted on the centrally-cracked specimens and MSD specimens.

• Steel and Composite Structures
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• 제33권3호
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• pp.445-461
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• 2019

The ultrasonic guided wave-based technique has become one of the most promising methods in non-destructive evaluation and structural health monitoring, because of its advantages of large area inspection, evaluating inaccessible areas on the structure and high sensitivity to small damage. To further advance the development of damage detection technologies using ultrasonic guided waves for the inspection of welded components in structures, the transmission characteristics of the ultrasonic guided waves propagating through welded joints with various types of defects or damage in steel plates are studied and presented in this paper. A three-dimensional (3D) finite element (FE) model considering the different material properties of the mild steel, high strength steel and austenitic stainless steel plates and their corresponding welded joints as well as the interaction condition of the steel plate and welded joint, is developed. The FE model is validated against analytical solutions and experimental results reported in the literature and is demonstrated to be capable of providing a reliable prediction on the features of ultrasonic guided wave propagating through steel plates with welded joints and interacting with defects. Mode conversion and scattering analysis of guided waves transmitted through the different types of weld defects in steel plates are performed by using the validated FE model. Parametric studies are undertaken to elucidate the effects of several basic parameters for various types of weld defects on the transmission performance of guided waves. The findings of this research can provide a better understanding of the transmission behaviour of ultrasonic guided waves propagating through welded joints with defects. The method could be used for improving the performance of guided wave damage detection methods.

• 소성∙가공
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• 제22권1호
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• pp.5-10
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• 2013

Steel plates are widely used in many manufacturing areas such as ship and bridge construction industries and are fabricated by different forming processes. Steel plates can have various shape defects, such as curl or camber. Roller leveling reduces the magnitude of the residual stress by using small amounts of reverse bending via an appropriate arrangement of the rolls and the associated plastic deformation in the steel plate. In this study a model for the residual stress after roller leveling is developed. In order to simplify the formulation, a plane-strain condition is assumed and the stress in the thickness direction is assumed to be negligible. The camber deformation in a real sized plate are measured and compared with the prediction values from the model to validate the accuracy of the model.

• 소성∙가공
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• 제22권7호
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• pp.377-382
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• 2013

Tension leveling is the process that removes the shape defects such as edge waves and center buckles, which may be formed in the rolled strip. The main purpose of tension leveling is to eliminate the differences in elongation in order to reduce the residual stresses. In this paper, a new approach for the optimization of the process conditions in tension leveling is presented. This new approach is an analytic model that predicts the residual stresses from the strip curvature. The prediction accuracy of the proposed model is examined through comparison with the predictions from a finite element model.

• Journal of Information Processing Systems
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• 제14권3호
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• pp.751-770
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• 2018

Web applications are indispensable in the software industry and continuously evolve either meeting a newer criteria and/or including new functionalities. However, despite assuring quality via testing, what hinders a straightforward development is the presence of defects. Several factors contribute to defects and are often minimized at high expense in terms of man-hours. Thus, detection of fault proneness in early phases of software development is important. Therefore, a fault prediction model for identifying fault-prone classes in a web application is highly desired. In this work, we compare 14 machine learning techniques to analyse the relationship between object oriented metrics and fault prediction in web applications. The study is carried out using various releases of Apache Click and Apache Rave datasets. En-route to the predictive analysis, the input basis set for each release is first optimized using filter based correlation feature selection (CFS) method. It is found that the LCOM3, WMC, NPM and DAM metrics are the most significant predictors. The statistical analysis of these metrics also finds good conformity with the CFS evaluation and affirms the role of these metrics in the defect prediction of web applications. The overall predictive ability of different fault prediction models is first ranked using Friedman technique and then statistically compared using Nemenyi post-hoc analysis. The results not only upholds the predictive capability of machine learning models for faulty classes using web applications, but also finds that ensemble algorithms are most appropriate for defect prediction in Apache datasets. Further, we also derive a consensus between the metrics selected by the CFS technique and the statistical analysis of the datasets.

• 산업경영시스템학회지
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• 제45권4호
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• pp.157-166
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• 2022

In the case of a die-casting process, defects that are difficult to confirm by visual inspection, such as shrinkage bubbles, may occur due to an error in maintaining a vacuum state. Since these casting defects are discovered during post-processing operations such as heat treatment or finishing work, they cannot be taken in advance at the casting time, which can cause a large number of defects. In this study, we propose an approach that can predict the occurrence of casting defects by defect type using machine learning technology based on casting parameter data collected from equipment in the die casting process in real time. Die-casting parameter data can basically be collected through the casting equipment controller. In order to perform classification analysis for predicting defects by defect type, labeling of casting parameters must be performed. In this study, first, the defective data set is separated by performing the primary clustering based on the total defect rate obtained during the post-processing. Second, the secondary cluster analysis is performed using the defect rate by type for the separated defect data set, and the labeling task is performed by defect type using the cluster analysis result. Finally, a classification learning model is created by collecting the entire labeled data set, and a real-time monitoring system for defect prediction using LabView and Python was implemented. When a defect is predicted, notification is performed so that the operator can cope with it, such as displaying on the monitoring screen and alarm notification.