• 한국주거학회논문집
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• 제14권4호
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• pp.11-19
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• 2003

This study intends to predict prospective defects and establish the plan of Preventive Maintenance through research and analysis of defect occurrences and their repair costs in the public equipment of ‘H’ apartment house from 1998 to 2001. According to results of the analysis, more than 90% of defects and their repair costs for 4 years of the building have occurred in heating, hot water and water supply equipments. In case of specific classification in each equipment, more than 60% of defects were found at hot water pipes and heating pipes, and their repair costs covered more than 60% of the total defect costs. After two repairs by ‘Preventive Maintenance’ had been performed in the year 1998, total defects and defects of each equipment each yew have increased in number from 1999 to 2001. But total repair costs and repair costs of each equipment have not increased as time has gone by, because repair costs have relationship with the price of materials and labor, the part of defect and the scale of repair.

• 비파괴검사학회지
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• 제23권3호
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• pp.270-279
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• 2003

Parametric studies have been conducted into the variability of the factors affecting the ultrasonic testing applied to weldments. The influence of ultrasonic equipment, transducer parameters, test technique, job parameters, defect type and characteristics on reliability far defect detection and sizing was investigated by experimentation. The investigation was able to build up substantial bank of information on the reliability of manual ultrasonic method for testing weldments. The major findings of the study separate into two parts, one dealing with correlation between ultrasonic techniques, equipment and defect parameters and inspection performance effectiveness and other with human factors. Defect detection abilities are dependent on the training, experience and proficiency of the UT operators, the equipment used, the effectiveness of procedures and techniques.

• 반도체디스플레이기술학회지
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• 제21권4호
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• pp.168-173
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• 2022

As the function of a product is advanced and the process is refined, the yield in the fine manufacturing process becomes an important variable that determines the cost and quality of the product. Since a fine manufacturing process generally produces a product through many steps, it is difficult to find which process or equipment has a defect, and thus it is practically difficult to ensure a high yield. This paper presents the system architecture of how to build a smart manufacturing system to analyze the big data of the manufacturing plant, and the equipment factor analysis methodology to increase the yield of products in the smart manufacturing system. In order to improve the yield of the product, it is necessary to analyze the defect factor that causes the low yield among the numerous factors of the equipment, and find and manage the equipment factor that affects the defect factor. This study analyzed the key factors of abnormal equipment that affect the yield of products in the manufacturing process using the data mining technique. Eventually, a methodology for finding key factors of abnormal equipment that directly affect the yield of products in smart manufacturing systems is presented. The methodology presented in this study was applied to the actual manufacturing plant to confirm the effect of key factors of important facilities on yield.

• 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.

• 한국지능시스템학회논문지
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• 제19권2호
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• pp.259-264
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• 2009

Mobile용 TFT-LCD는 근거리에서, 세밀한 관찰 작업용으로 사용되는 경우가 많아 높은 수준의 품질관리가 요구되고 있으나, 높은 휘도값, 큰 휘도편차, 높은 검사 정밀도 등의 특징을 가지고 있어 동일한 검사기준을 적용하여도 작업자 혹은 제조라인에 따라 판단의 차이가 있으며 정량적인 품질관리가 어렵다. 또한, 다품종 대량생산 추세에 따라 검사 속도, 작업자의 피로도, 검사 시야의 한계 등 육안검사의 문제점이 대두되고 있다. 본 논문은 Mobile용 TFT-LCD 화면의 품질관리 및 검사기준과 동일한 기준에 의거하여 현장에 적용하기 쉬운 탁상형의 Mobile용 TFT-LED 화면 검사 장비를 개발하였다. 그리고 개발된 장비를 사용한 실험에서, 육안검사에 비하여 개선된 결과를 기반으로 안정적이고 수치화된 Mobile용 TFT-LCD 화면 품질 검사의 표준화 가능성을 제시한다.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2001년도 하계학술대회 논문집 B
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• pp.819-821
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• 2001

This paper describes a characteristics of MFL according to the position of Hall sensor Magnetic Flux Leakage(MFL) Method is used to detect surface defect in ferromagnetic plate. A plate has a surface defect and magnetizing equipment are producted to perform Non-Destructive Testing(NDT) using MFL. The SM 45C carbon steel plate is adopted to this experiment. there is a artifical defect with a twice of thickness and a half of depth of plate. Magnetizing equipment is composed of yoke made by layer-built of silicon sheet steel, NdFeB magnetic and iron brushes. Detecting defect is performed by MFL NDT using Hall sensor. It is shown that magnetic flux detected by Hall sensor is affected according to the position of Hall sensor through MFL experiment and numerical analysis.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2001년도 춘계학술대회 논문집 전기기기 및 에너지변환시스템부문
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• pp.126-128
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• 2001

In this paper, Magnetic Flux Leakage(MFL) method is used to detect surface defect in ferromagnetic plate. Surface defects are created on the SM 45C ferromagnetic plate and magnetizing equipment is composed to perform MFL nondestructive testing. The length and width of defect is twice the thickness of ferromagnetic plate, and defects with different depths are made artificially for the experiment. Also, NdFeB magnet in magnetizing equipment is used to make magnetic flux. This paper shows that it is possibile to detect 10% defect and to analyze numerically for any defect using MFL method.

• Journal of Information Processing Systems
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• 제16권5호
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• pp.1008-1014
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• 2020

Aiming at the defect detection quality of denim fabric, this paper designs an improved algorithm based on the optimized Gabor filter. Firstly, we propose an improved defect detection algorithm of jean fabric based on the maximum two-dimensional image entropy and the loss evaluation function. Secondly, 24 Gabor filter banks with 4 scales and 6 directions are created and the optimal filter is selected from the filter banks by the one-dimensional image entropy algorithm and the two-dimensional image entropy algorithm respectively. Thirdly, these two optimized Gabor filters are compared to realize the common defect detection of denim fabric, such as normal texture, miss of weft, hole and oil stain. The results show that the improved algorithm has better detection effect on common defects of denim fabrics and the average detection rate is more than 91.25%.

• 정보처리학회논문지A
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• 제17A권2호
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• pp.81-92
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• 2010

진동 정보를 통해 기계 설비의 상태나 고장 유무를 판단하는 연구들이 다수 진행 중에 있는데, 대부분의 연구에서는 설비에 대한 진동을 모니터링하거나 고장 유무를 판별하여 사용자에게 알리는 수준이다. 본 논문에서는 진동에 의한 고장 진단과 판별을 보다 정교하게 수행하는 선박 엔진의 고장 감지 기법과 시스템을 제안하였다. 일차적으로 이중적 진동 정보 판별 기법을 적용하여 진동 정보를 확인한 다음에 고장 유무를 검사한다. 만일 고장이 발생한 경우에는 진동 정보의 오류 부분만을 이용하여 고장 진동 파형에 대한 오차 범위를 기준으로 어떤 유형의 고장인지를 판별할 수 있는 기법을 적용하였다. 또한 선박의 진동 경향 분석과 엔진 안전 보존을 목적으로 진동 정보를 데이터베이스에 저장하고 추적할 수 있도록 시스템을 구현하였다. 제안 시스템을 선박 엔진의 고장 판별 유무와 고장 진동 파형 감별 인자에 대해 실험을 수행한 결과 고장 유무 판별은 약 100% 정확성을 가졌고 고장 진동 파형의 유형 인식에서는 약 96% 정확성을 가졌다.

• 한국반도체및디스플레이장비학회:학술대회논문집
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• 한국반도체및디스플레이장비학회 2004년도 춘계학술대회 발표 논문집
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• pp.59-62
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• 2004

The defect formation energy of boron nitride (BN) nanotubes is investigated using molecular-dynamics simulation. Although the defect with tetragon-octagon pairs (4-88-4) is favored in the flat cap of BN nanotubes, BN clusters, and the growth of BN nanotubes, the formation energy of the 4-88-4 defect is significantly higher than that of the pentagon-heptagon pairs (5-77-5) defect in BN nanotubes. The 5-77-5 defect reduces the effect of the structural distortion caused by the 4-88-4 defect, in spite of homoelemental bonds. The instability of the 4-88-4 defect generates the structural transformation into BNNTs with no defect at about 1500 K.