• 산업공학
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• 제20권3호
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• pp.288-297
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• 2007

Success of semiconductor/LCD industry depends on its yield and quality of product. For the purpose, FDC (Fault Detection and Classification) system is used to diagnose fault state in main manufacturing processes by monitoring time series data collected by equipment sensors which represent various conditions of the equipment. The data set is segmented at the start and end of each product lot processing by a trigger event module. However, in practice, segmented sensor data usually have the features of data asynchronization such as different start points, end points, and data lengths. Due to the asynchronization problem, false alarm (type I error) and missed alarm (type II error) occur frequently. In this paper, we propose a robust process fault detection system by integrating a process event detection method and a similarity measuring method based on dynamic time warping algorithm. An experiment shows that the proposed system is able to recognize abnormal condition correctly under the asynchronous data situation.

• International Journal of Internet, Broadcasting and Communication
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• 제14권3호
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• pp.91-100
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• 2022

Major developed countries are seriously considering smart factories to increase their manufacturing competitiveness. Smart factory is a customized factory that incorporates ICT in the entire process from product planning to design, distribution and sales. This can reduce production costs and respond flexibly to the consumer market. The smart factory converts physical signals into digital signals, connects machines, parts, factories, manufacturing processes, people, and supply chain partners in the factory to each other, and uses the collected data to enable the smart factory platform to operate intelligently. Enhancing personalized value is the key. Therefore, it can be said that the success or failure of a smart factory depends on whether big data is secured and utilized. Standardized communication and collaboration are required to smoothly acquire big data inside and outside the factory in the smart factory, and the use of big data can be maximized through big data analysis. This study examines big data analysis and standardization in smart factory. Manufacturing innovation by country, smart factory construction framework, smart factory implementation key elements, big data analysis and visualization, etc. will be reviewed first. Through this, we propose services such as big data infrastructure construction process, big data platform components, big data modeling, big data quality management components, big data standardization, and big data implementation consulting that can be suggested when building big data infrastructure in smart factories. It is expected that this proposal can be a guide for building big data infrastructure for companies that want to introduce a smart factory.

• 한국신뢰성학회지:신뢰성응용연구
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• 제16권2호
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• pp.98-103
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• 2016

Purpose: The purpose of this paper is to get a meaningful information for improving manufacturing quality of the products before they are produced in client's manufacturing process. Methods: A variety of data mining techniques have been being used for wide range of industries from process data in manufacturing factories for quality improvement. One application of those is to get meaningful information from process data in manufacturing factories for quality improvement. In this paper, the failure rate at client's manufacturing process is predicted by using the parameters of the characteristics of the product based on PCA (Principle Component Analysis) and regression analysis. Results: Through a case study, we proposed the predicting methodology and regression model. The proposed model is verified through comparing the failure rates of actual data and the estimated value. Conclusion: This study can provide the guidance for predicting the failure rate on the manufacturing process. And the manufacturers can prevent the defects by confirming the factor which affects the failure rate.

• 대한기계학회논문집A
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• 제35권1호
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• pp.39-45
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• 2011

제품에 따라 상이한 생산공정과 각 공정에서 발생하는 다양한 정보를 관리하는 제조실행시스템(MES) 구현을 위해서는 제조 시스템의 특성을 고려한 데이터 수집 시스템(Data Acquisition System)이 필요하다. 본 논문에서는 작업지시부터 작업실적보고 사이에서 발생하는 생산현장 정보를 실시간으로 수집하고 처리하여 MES 에 제공하는 설비정보 수집 시스템을 소개한다. 제안 시스템은 다양한 설비 정보를 실시간으로 처리하는 데이터 파서 모듈, 이를 작업실적정보로 생성하는 데이터 맵퍼 모듈, 생성된 작업실적정보를 상위 시스템인 MES, ERP 에 제공하는 SOA 기반 데이터 연동 모듈로 구성된다. 시스템의 시범적용 결과, 설비나 공정의 추가, 변경에도 쉽게 재구성 가능하고 유지보수가 용이하였다.

• 한국정보전자통신기술학회논문지
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• 제14권4호
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• pp.292-299
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• 2021

IT기술의 발달로 제조 기반의 IIoT환경을 기반으로 한 데이터 마이닝 형태의 소프트웨어들이 점차 늘어나고 있다. 그러나 빅데이터 및 데이터마이닝을 진행해야 하는 대량의 데이터를 가지는 제조 기업의 소프트웨어 특성상 일반 소프트웨어와 동일한 형태로 소프트웨어 품질을 평가하기 힘든 실정이다. 또한 이기종간의 장비 및 소프트웨어가 혼재된 제조 기반의 환경에서 특히 기존의 품질 특성을 적용하여 사용되는 소프트웨어에 대한 품질 판단을 진행하기 어렵다. 본 논문에서는 제조 기반의 특성을 조사하고 이에 맞는 소프트웨어 품질 평가 모델을 개발하여 평가를 실시하고자 한다.

• 산업경영시스템학회지
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• 제37권2호
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• pp.55-61
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• 2014

Recently, the manufacturing system is being changed in a mass customization and small quantity batch production. MES is a powerful production management tool supporting production optimization from the process initiation to the final shipment. It is a production management system which plans and executes based on the production data in the shop floor. This study deployed the utilization of production data and web HMI system to process real-time production data through the collection with the shop floor. The developed system was applied to the equipment operating time and other production data could be processed with the real-time. The proposed system and web HMI can be applied for various production systems by using different logic.

• 대한임베디드공학회논문지
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• 제14권4호
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• pp.187-195
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• 2019

In the fourth industrial revolution era, various commercial smart platforms for smart system implementation are being developed and serviced. However, since most of the smart platforms have been developed for general purposes, they are difficult to apply / utilize because they cannot satisfy the requirements of real-time data management, data visualization and data storage of smart factory system. In this paper, we implemented an open source based smart manufacturing big data platform that can manage highly efficient / reliable data integration for the diagnosis diagnostic system of manufacturing robots.

• 한국정밀공학회:학술대회논문집
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• 한국정밀공학회 2006년도 춘계학술대회 논문집
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• pp.611-612
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• 2006

Many data mining techniques have been proved useful in revealing important patterns from large data sets. Especially, data mining techniques play an important role in a customer data analysis in a financial industry and an electronic commerce. Also, there are many data mining related research papers in a semiconductor industry and an automotive industry. In addition, data mining techniques are applied to the bioinformatics area. To satisfy customers' various requirements, each industry should develop new processes with more accurate production criteria. Also, they spend more money to guarantee their products' quality. In this manner, we apply data mining techniques to the production-related data such as a test data, a field claim data, and POP (point of production) data in the automotive parts industry. Data collection and transformation techniques should be applied to enhance the analysis results. Also, we classify various types of manufacturing processes and proposed an analysis scheme according to the type of manufacturing process. As a result, we could find inter- or intra-process relationships and critical features to monitor the current status of the each process. Finally, it helps an industry to raise their profit and reduce their failure cost.

• 산업경영시스템학회지
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• 제26권3호
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• pp.67-73
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• 2003

Manufacturing technologies of compound semiconductor are similar to the process of memory device, but management technology of manufacturing process for compound semiconductor is not enough developed. Semiconductor manufacturing environment also has been emerged as mass customization and open foundry service so integrated manufacturing system is needed. In this study we design the integrated manufacturing system for compound semiconductor fabrication t hat has monitoring of process, reduction of lead-time, obedience of due-dates and so on. This study presents integrated manufacturing system having database system that based on web and data acquisition system. And we will implement them in the actual compound semiconductor fabrication.

• 대한기계학회논문집A
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• 제35권9호
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• pp.1063-1070
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• 2011

생산시스템은 다양한 제품을 특정 공정에 따라 생산되며, 이는 다양한 생산 데이터를 발생시킨다. 이러한 데이터를 효율적으로 관리하기 위해 제조실행시스템(MES)과 같은 생산관리시스템이 사용된다. MES에서 가장 중요한 항목은 생산설비에서 발생되는 설비데이터를 작업자의 간섭 없이 직접적으로 수집하는 시스템이다. 본 논문에서는 생산현장에서 발생하는 다양한 정보를 실시간으로 수집하기 위한 데이터 수집 방법으로 범용기계에 적용 가능한 센서를 이용한 방법과 자동화 기계에 적용 가능한 PLC를 이용한 데이터수집 방법을 제안하였다. 제안된 데이터 수집시스템은 실제 생산시스템에 적용하여 효율성을 검증하였다. 제조 현장에서 설비의 상태데이터를 수집하는 시스템을 활용하여 실시간으로 현장정보를 제공할 수 있는 MES를 구현할 수 있다.