• Journal of the Korean housing association
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• v.14 no.4
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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.

• Journal of the Korean Society for Nondestructive Testing
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• v.23 no.3
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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.

• Journal of the Semiconductor & Display Technology
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• v.21 no.4
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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.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.45 no.4
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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.

• Journal of the Korean Institute of Intelligent Systems
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• v.19 no.2
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• pp.259-264
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• 2009

High level quality control is required for mobile TFT-LCD modules which are frequently used for fine observation. However, quantitative quality control is difficult. Defect inspection using naked eyes makes irregular inspection results. This paper developed desk type defect inspection equipment for mobile TFT-LCD modules using the same inspection criterion with that of naked eyes. From experiments using this equipments, possibilities of standardization in defect inspection equipment for mobile TFT-LCD modules are presented.

• Proceedings of the KIEE Conference
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• 2001.07b
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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.

• Proceedings of the KIEE Conference
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• 2001.04a
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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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• v.16 no.5
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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%.

• The KIPS Transactions:PartA
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• v.17A no.2
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• pp.81-92
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• 2010

There has been some research in the equipment defect detection based on vibration information. Most research of them is based on vibration monitoring to determine the equipment defect or not. In this paper, we introduce more accurate system for engine defect detection based on vibration information and we focus on detection of engine defect for boat and system control. First, it uses the duplicated-checking method for vibration information to determine the engine defect or not. If there is a defect happened, we use the method using error part of vibration information basis with error range to determine which kind of error is happened. On the other hand, we use the engine trend analysis and standard of safety engine to implement the vibration information database. Our simulation results show that the probability of engine defect determination is 100% and the probability of engine defect classification and detection is 96%.

• Proceedings of the Korean Society Of Semiconductor Equipment Technology
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• 2004.05a
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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.