• 제어로봇시스템학회논문지
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• 제6권2호
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• pp.207-216
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• 2000

This paper introduces an effective system design method to develop a customer oriented product using a design optimization process and to select a set of critical design paramenters,. The process results in the development of a successful product satisfying customer needs and reducing development risk. The proposed scheme adopted a five step QFD(Quality Function Deployment) in order to extract design parameters from customer needs and evaluated their priority using risk factors for extracted design parameters. In this process we determine critical design parameters and allocate them to subsystem designers. Subsequently design engineers develop and test the product based on these parameters. These design parameters capture the characteristics of customer needs in terms of performance cost and schedule in the process of QFD, The subsequent risk management task ensures the minimum risk approach in the presence of design parameter uncertainty. An application of this approach was demonstrated in the development of weld quality monitoring system. Dominant design parameters affect linearity characteristics of weld defect feature vectors. Therefore it simplifies the algorithm for adopting pattern classification of feature vectors and improves the accuracy of recognition rate of weld defect and the real time response of the defect detection in the performance. Additionally the development cost decreases by using DSP board for low speed because of reducing CPU's load adopting algorithm in classifying weld defects. It also reduces the cost by using the single sensor to measure weld defects. Furthermore the synergy effect derived from the critical design parameters improves the detection rate of weld defects by 15% when compared with the implementation using the non-critical design parameters. It also result in 30% saving in development cost./ The overall results are close to 95% customer level showing the effectiveness of the proposed development approach.

• 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.

• 디지털융복합연구
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• 제10권11호
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• pp.271-277
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• 2012

본 연구의 목적은 콘센트용 아크로 플레이트공정의 불량률을 줄이고 생산공정 개선을 통하여 생산성 향상시키는데 있다. 사출과정에서 사출시 온도와 압력에 대한 실험계획으로 최적의 조건을 찾아내고 아크로 플레이트 금형 개선을 하여 생산성을 향상시켰다. 본 연구에서는 사이클 타임은 70초에서 51초 줄었고 불량률은 20%에서 0.5%로 감소하였으며 생산원가 240원에서 201원으로 절감 효과를 가져왔다.

• 한국인터넷방송통신학회논문지
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• 제18권3호
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• pp.101-110
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• 2018

최근 소프트웨어 개발시 CMMI 수준에 관한 인증 여부는 조직 경쟁력의 핵심적인 요인으로 그 중요성이 지속적으로 강조되고 있다. 따라서, 소프트웨어 개발시 CMMI에 기반한 프로세스 개선 활동을 활발하게 적용하고 있는 추세이다. 본 논문에서는 정량적 프로세스 관리 단계인 CMMI Level 4에 기반한 프로세스 개선활동을 목표로 하고 있으며, 이를 위한 조직구성이나 프로세스의 적용방법에 대한 내용을 실제 개발시 실적용을 통해 연구한 방법을 제시하고 있다. 이로써 기준만 제시하고 있는 모델에 대한 실제 적용 방법을 쉽게 이해 할 수 있게 하였다. 또한, 정성적 프로젝트 관리가 아닌 정량적 프로젝트 관리에 따른 그 개선 효과를 나타내고자 일정/공수 분석, 동료검토활동을 통한 결함관리의 적용 사례분석을 수행하였다. 끝으로 실제 적용에 따른 문제점과 개선방향도 제시하였다. 본 연구 결과는 CMMI에서 언급하고 있는 정량적 프로세스 관리의 효과부분을 검증하는데 도움이 될 수 있으며, 향후 CMMI에 기반한 프로세스 개선을 도입 예정인 조직에 유용하게 활용될 수 있을 것으로 기대한다.

• 반도체디스플레이기술학회지
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• 제21권2호
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• pp.74-84
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• 2022

As the semiconductor industry develops, the difficulty of newly required process technology becomes difficult, and the importance of production yield and product reliability increases. As an effort to minimize yield loss in the manufacturing process, interests in the process defect process for facility diagnosis and defect identification are continuously increasing. This research observed the plasma condition changes in the multi oxide/nitride layer deposition (MOLD) process, which is one of the 3D-NAND manufacturing processes through optical emission spectroscopy (OES) and monitored the result of whether the change in plasma characteristics generated in repeated deposition of oxide film and nitride film could directly affect the film. Based on these results, it was confirmed that if a change over a certain period occurs, a change in the plasma characteristics was detected. The change may affect the quality of oxide film, such as the film thickness as well as the interfacial surface roughness when the oxide and nitride thin film deposited by plasma enhenced chemical vapor deposition (PECVD) method.

• 산업경영시스템학회지
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• 제46권4호
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• pp.312-320
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• 2023

Recently, there has been an increasing attempt to replace defect detection inspections in the manufacturing industry using deep learning techniques. However, obtaining substantial high-quality labeled data to enhance the performance of deep learning models entails economic and temporal constraints. As a solution for this problem, semi-supervised learning, using a limited amount of labeled data, has been gaining traction. This study assesses the effectiveness of semi-supervised learning in the defect detection process of manufacturing using the MixMatch algorithm. The MixMatch algorithm incorporates three dominant paradigms in the semi-supervised field: Consistency regularization, Entropy minimization, and Generic regularization. The performance of semi-supervised learning based on the MixMatch algorithm was compared with that of supervised learning using defect image data from the metal casting process. For the experiments, the ratio of labeled data was adjusted to 5%, 10%, 25%, and 50% of the total data. At a labeled data ratio of 5%, semi-supervised learning achieved a classification accuracy of 90.19%, outperforming supervised learning by approximately 22%p. At a 10% ratio, it surpassed supervised learning by around 8%p, achieving a 92.89% accuracy. These results demonstrate that semi-supervised learning can achieve significant outcomes even with a very limited amount of labeled data, suggesting its invaluable application in real-world research and industrial settings where labeled data is limited.

• 유통과학연구
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• 제17권4호
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• pp.51-57
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• 2019

Purpose - This study aims to improve the distribution structure of the OLED market and develop cost-effective optimization techniques. Specifically, it is a study on the optimization of ferric chloride to improve the etch of SUS MASK for OLED. Research design, data, and methodology - Applying the optimal conditions of the experiment, the final confirmation was evaluated for improvement by the Process Capability Index (Cpk). It is possible to derive social performance such as improvement of precision of SUS MASK manufacturing, economic performance such as defect rate, reduction of waste generation and treatment cost, technological achievement such as SUS MASK production technology, improvement of profit structure of technology development and process improvement do. Results - The improvement of the Cpk before the improvement was made was confirmed to be 0.57% with a defect estimate of 25.07% with a failure estimate of 0.57% after the improvement, and 8.84% with a failure estimate of 0.57% level after the improvement. Conclusions - If the conclusions obtained from the specimen experiment are applied to the manufacturing process of SUS MASK, it will be possible to expect excellent cost-effective competitiveness due to the improvement of precision and reduction of defect rate to enhance the OLED market penetration.

• Industrial Engineering and Management Systems
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• 제9권3호
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• pp.251-261
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• 2010

This article demonstrated the application of the Fuzzy Data Envelopment Analytical Hierarchy Process (FDEAHP) to evaluate the root causes of critical defect problems occurring in the production of liquid medicine. The methodology of the research began by collecting the defect data by using Check Sheets, and ranking the significant problems by using a Pareto Diagram. Two types of major problems were found to occur, including glass fragments in the medicine and damaged lid threads. The causes of each problem were then analyzed by using Cause and Effect Diagrams. The significant causes were ranked by FDEAHP under three criteria, Severity (S), Occurrence (O) and Detection (D), followed by the framework of the FMEA Technique. Two causes with the highest Final Weight (FW) of each problem were selected to be improved, such as installing auxiliary equipment, using the Poka-Yoke system, setting the scale of the shaft and lathing the bushes of each bottle size. The results demonstrated a reduction in defects from 3.209% to 1.669% and showed that improving a few significant root causes, identified by an experienced decision maker, was sufficient to reduce the defect rate.

• Journal of the Korean Data and Information Science Society
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• 제18권3호
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• pp.773-782
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• 2007

The statistical process control charts are very extensively used for monitoring of process mean, deviation, defect rate or reject rate. In this paper we consider a control chart to monitor the process reject rate in the high yield process, which is based on the observed cumulative probability of the number of items inspected until r defective items are observed. We first propose selection of the optimal value of r in the CPC-r charts, and also consider the usefulness of the chart in high yield process such as semiconductor or TFT-LCD manufacturing process.

• 디지털산업정보학회논문지
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• 제9권4호
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• pp.239-249
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• 2013

The role of software process is getting more important to make good quality softwares. One of the measures to improve the software process is Defect Removal Efficiency(DRE). DRE gives a measure of the development team ability to remove defects prior to release. It is calculated as a ratio of defects resolved to total number of defects found. Software Engineering Levels are usually decided by CMMI Model. The model is designed to help organizations improve their software product and service development, acquisition, and maintenance processes. The score of software engineering levels can be calculated by CMMI model. The levels are composed of the three groups(absent, average, and advanced). This study is to find if there is any difference among the three categories in term of the result of software engineering levels on DRE. We propose One way ANOVA to analyze influence of software engineering levels on DRE. Bootstrap method is also used to estimate the sampling distribution of the original sample because the data are not sampled randomly. The method is a statistical method for estimating the sampling distribution of an estimator by sampling with replacement from the original sample. The data were collected in 106 software development projects by the survey. The result of this study tells that there is some difference of DRE among the groups. The higher the software engineering level of a specific company becomes, the better its DRE gets, which means that the companies trying to improve software process can increase their good management performance.