• Journal of Information Processing Systems
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• 제15권1호
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• pp.181-188
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• 2019

A production system is a management system that supports all activities to perform production operations at the manufacturing site. From the point-of-view of a smart factory, smart manufacturing systems redesigned the concept of onsite production systems to fit the entire system and its necessary functional composition. In this study, we select the key functions needed to build a smart factory for a PCB line and propose a new six-step model for the deployment of a smart manufacturing system by integrating essential functions. The smart manufacturing system newly classified the production and operation tasks of PCB manufacturing and selected necessary functions through requirement analysis and benchmarking of advanced companies. The selected production operation tasks are mapped to the functions of the system and configured into seven modules, and the optimal deployment model is presented to allow flexible responses to the characteristics of the tasks. These methodologies are first presented in this study, and the proposed model was applied to the PCB line to confirm that they had significant changes in the work method, qualitative effects, and quantitative effects. Typically, lead time and WIP have reduced by about 50%.

• 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.

• 시스템엔지니어링학술지
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• 제13권2호
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• pp.57-64
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• 2017

A new paradigm based on distributed manufacturing services is emerging. This paradigm shift can be realized by smart functions and smart technologies such as Cyber Physical System (CPS), Artificial Intelligence (AI), and Cloud Computing. Most architectures define stack levels from Level 0 (equipment) to Level 4 (business area) and specify the services to be provided between them. Because of their a rough technical specification, there is a limitation on how to actually utilize a technology to actually implement a smart factory service with this architecture. In this paper, we propose a smart factory architecture that can be utilized directly from the perspective of a smart service system by making the use of System Engineering Process and System Modeling Language (SysML).

• 한국전자통신학회논문지
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• 제16권5호
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• pp.783-788
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• 2021

스마트제조는 제품기획, 설계, 생산, 품질, 재고, 조달 등 스마트공장 내 제조 프로세스의 정보화, 최적화 및 생산 시스템의 자동화를 ICT기반 스마트 기술로 실현하는 제조 공정으로 정의된다. 본 논문에서는 스마트공장과 스마트제조 시스템의 운영시 발생하는 제조 데이터와 관련된 국내 표준화 동향에 대해 소개한다. 표준화의 범위가 매우 넓기 때문에 스마트공장 및 제조 ICT시스템과 관련된 일반적인 표준화 내용 및 제조 데이터를 거래시 필요한 표준에 관련된 사항을 다룬다. 이를 기반으로 하여 제조 데이터의 활용을 위해 필요한 사항에 대해 논의한다.

• 한국전자거래학회지
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• 제24권2호
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• pp.165-178
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• 2019

스마트공장 등으로 변화하는 경쟁 환경 속에서 제조시스템의 현 수준을 측정하고 개선 목표와 과제를 도출, 추진하여 제조경쟁력의 수준을 높이는 것이 기업의 기본적 활동이 되고 있다. 그러나 기업의 미래 제조경쟁력을 갖추기 위한 구성요소 분석과 성숙도평가에 관한 연구는 부분적으로 진행되고 있고 초기단계에 있다. 본 연구는 제조시스템에 대한 다양한 관점의 모델, 개발프로세스, 프레임워크 등에 대한 기존 연구를 분석하였다. 또한 스마트공장 관련 성숙도평가 연구들을 통해 미래 제조시스템의 구성요소들을 도출하여 구조모델을 설계하였다. 평가모델, 변환모델까지 포함하는 메타모델을 설계하고 프레임워크 개발 프로세스를 도출하여 미래 제조시스템의 성숙도평가를 위한 통합적 프레임워크를 제안하였다. 또한 실제 스마트공장 평가에 적용하여 검증하였다. 제시된 프레임워크는 미래 제조시스템의 성숙도평가를 위한 기반 도구로 활용될 수 있을 것이다.

• 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.

• 산업경영시스템학회지
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• 제42권4호
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• pp.76-83
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• 2019

The South Korean government is actively assisting the supply of the smart factory solutions to SMEs (Small & Medium-sized Enterprises) according to its manufacturing innovation 3.0 policy for the smart manufacturing as the 4th industrial revolution era unfolds. This study analyzed the impacts of the smart factory solutions, which have been supplied by the government, on the companies performances. The effects of the level of smart factory and the operation capabilities for the smart factory solutions on company performances, and the mediating effects of manufacturing capabilities have been analyzed using SPSS and AMOS. The data for this survey-based study were collected from the SMEs which implemented the smart factory solutions since 2015. The results show that the level of smart factory solutions adopted and operation capabilities for the smart factories do not have direct effects on the company performances, but their mediating effects on the manufacturing capabilities matter and the manufacturing capabilities effect directly on the company performances. In addition significant factors boosting the operation capability for the smart factory and the levels of the smart factory solutions are identified. Finally, the policy direction for enhancing the smart factory effects is presented, and the future research directions along with the limitations are suggested.

• International Journal of Internet, Broadcasting and Communication
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• 제12권4호
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• pp.109-121
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• 2020

As a decrease in labor became a serious issue in the manufacturing industry, smart factory technology, which combines IT and the manufacturing business, began to attract attention as a solution. In this study, we have designed and implemented a real-time remote management system for smart factories, which is connected to an IoT sensor and gateway, for plastic manufacturing plants. By implementing the REST API in which an IoT sensor and smart gateway can communicate, the system enabled the data measured from the IoT sensor and equipment status data to the real-time monitoring system through the gateway. Also, a web-based management dashboard enabled remote monitoring and control of the equipment and raw material processing status. A comparative analysis experiment was conducted on the suggested system for the difference in processing speed based on equipment and measurement data number change. The experiment confirmed that saving equipment measurement data using cache mechanisim offered faster processing speed. Through the result our works can provide the basic framework to factory which need implement remote management system.

• 한국정보통신학회논문지
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• 제25권1호
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• pp.115-123
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• 2021

생산관리 시스템은 생산 공정 내의 모든 자원의 공정단위 생산계획을 현장에서 실행하는 것은 물론 생산 관련 품질 데이터까지 다루는 공장정보화시스템이다. 인공지능으로 자동화와 연결성이 극대화되는 4차 산업혁명이 화두가 되면서 제조업체들은 스마트 공장 구축에 관심을 보이나 막대한 구축비와 표준화되지 않은 생산 공정은 스마트 공장구축에 걸림돌이 되고 있다. 그래서 본 논문은 노후화된 공장에서 스마트 공장 구축을 위한 제조관리 시스템을 설계 및 구현한다. 철강 선재 공정을 위한 기초 수준의 스마트 공장을 목표로 Web 기반의 제조공정 시스템을 제안한다. 제안 시스템은 REST API를 사용하여 기존 구축된 ERP 시스템과의 연동을 원활히 지원할 것이며 다양한 기기와 다양한 브라우저에서 사용할 수 있도록 확장성을 고려할 것이다. 제안한 WoT 기반 생산관리시스템을 구현하여 실용성을 보이겠다.

• 한국멀티미디어학회논문지
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• 제25권2호
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• pp.197-205
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• 2022

The Cyber Physical System(CPS) is an important concept in achieving SMSs(Smart Manufacturing Systems). Generally, CPS consists of physical and virtual elements. The former involves manufacturing devices in the field space, whereas the latter includes the technologies such as network, data collection and analysis, security, and monitoring and control technologies in the cyber space. Currently, all these elements are being integrated for achieving SMSs in which we can control and analyze various kinds of producing and diagnostic issues in the cyber space without the need for human intervention. In this study, we focus on implementing a production equipment monitoring system related to building a SMS. First, we describe the development of a fog-based gateway system that links physical manufacturing devices with virtual elements. This system also interacts with the cloud server in a multimedia network environment. Second, we explain the proposed network infrastructure to implement a monitoring system operating on a cloud server. Then, we discuss our monitoring applications, and explain the experience of how to apply the ML(Machine Learning) method for predictive diagnostics.