• 한국전자통신학회논문지
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• 제14권1호
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• pp.199-204
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• 2019

본 논문에서는 개인의 일상생활에 필요한 컨텐츠를 제공하는 스마트 미러를 소개한다. 음성인식으로 지정해놓은 명령어를 입력하면 디스플레이에서 원하는 컨텐츠를 출력하는 스마트 미러를 제작하였다. 현재 제작한 스마트 미러의 컨텐츠는 시간과, 날씨, 지하철정보, 일정, 사진이 있다. 시중의 개인 가정용으로 판매하고 있는 스마트 미러는 비싼 가격으로 인해 보급이 어려운 상태이지만 본 논문에서 제시하는 스마트 미러 제작을 통해 제조 단가를 낮출 수 있으며, 음성인식으로 더 편리하게 이용할 수 있다.

• International journal of advanced smart convergence
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• 제8권1호
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• pp.159-165
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• 2019

'4th Industrial Revolution' is accelerating as a core part of creating new growth engines and enhancing competitiveness of businesses. The fourth industrial revolution means the transformation of society and industries that are brought by IoT (Internet of Things), big data analysis, AI (Artificial Intelligence), and robot technology. Information and Communication Technology (ICT), which is a major factor, is affecting production and manufacturing systems and as ICT technologies become more advanced, intelligent information technology is generally utilized in all areas of society, leading to hyper-connected society where new values are created and developed. ICT technology is not just about connecting devices and systems and making smart, it is about constantly converging and harmonizing new technologies in a number of fields and driving innovation and change. It is no exception to the agro-fisheries trade. In particular, ICT technology is applied to the agricultural sector, reducing labor, providing optimal environment for crops, and increasing productivity. Due to the nature of agriculture, which is a labor-intensive industry, it is predicted that the ripple effects of ICT technologies will become bigger. We are expected to use the Smart Concentration using ICT convergence technology as a useful resource for changing smart farms, and to help develop new service markets.

• 품질경영학회지
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• 제48권1호
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• pp.87-105
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• 2020

Purpose: This paper aims to propose a practical strategy for smart factories and a step-by-step quality strategy according to the maturity of smart factory construction. Methods: The characteristics, compositional requirements, and diagnosis system are examined for smart factories through theoretical considerations. Several cases of implementing smart factory are studied considering the company maturity level from the aspect of the smartness concept. And specific quality techniques and innovation activities are carefully reviewed. Results: The maturity level of smart factory was classified into five phases: 1) ICT non-application, 2) basic, 3) intermediate 1, 4) intermediate 2, 5) advanced level. A five-step quality strategy was established on the basis of case studies; identify, measure, analyze, optimize, and customize. Some quality techniques are introduced for step-by-step implementation of quality strategies. Conclusion: To build a successful smart factory, it is necessary to establish a quality strategy that suits the culture and size of the company. The quality management strategy proposed in this paper is expected to contribute to the establishment of appropriate strategies for the size and purpose of the company.

• 산업경영시스템학회지
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• 제44권2호
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• pp.43-57
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• 2021

Smart factories can be defined as intelligent factories that produce products through IoT-based data. In order to build and operate a smart factory, various new technologies such as CPS, IoT, Big Data, and AI are to be introduced and utilized, while the implementation of a MES system that accurately and quickly collects equipment data and production performance is as important as those new technologies. First of all, it is very essential to build a smart factory appropriate to the current status of the company. In this study, what are the essential prerequisite factors for successfully implementing a smart factory was investigated. A case study has been carried out to illustrate the effect of implementing ERP and MES, and to examine the extensibilities into a smart factory. ERP and MES as an integrated manufacturing information system do not imply a smart factory, however, it has been confirmed that ERP and MES are necessary conditions among many factors for developing into a smart factory. Therefore, the stepwise implementation of intelligent MES through the expansion of MES function was suggested. An intelligent MES that is capable of making various decisions has been investigated as a prototyping system by applying data mining techniques and big data analysis. In the end, in order for small and medium enterprises to implement a low-cost, high-efficiency smart factory, the level and goal of the smart factory must be clearly defined, and the transition to ERP and MES-based intelligent factories could be a potential alternative.

• 융합정보논문지
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• 제11권11호
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• pp.172-177
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• 2021

디지털시대의 일환으로 현실세계를 가상으로 정확하게 옮겨서 현실에서 할 수 없는 어떤 고가의 장비 수명을 저하시키는 스트레스 테스트를 실시하여 제품의 취약부분을 시뮬레이션 하는 디지털 트윈이 제조업에서 활발히 사용되고 있다. IoT의 발전으로 인해 현실세계에서 수집되는 데이터를 정확하게 수집하여 가상의 공간에 동일하게 만드는 디지털 트윈은 교통, 재난, 주거, 방역, 에너지, 환경, 고령화 같은 도시 생활의 문제에 대한 정확한 예측을 할 수 있기 때문에 스마트 시티 건설에 필요한 도구로 자리매김하고 있다. 디지털 트윈은 제조업분야에 적용이 많이 되고 있지만 본 연구에서는 스마트 시티에 적용하여 4차산업혁명시대에 걸 맞는 스마트 시티 모델을 제안하고 제안된 모델을 통한 시민의 안전, 복지와 편리성을 증가시켜 기술의 발전이 삶의 질을 향상시키는 데 기여하고 한다. 또한, 디지털 트윈을 스마트 시티에 적용할 경우 실시간 상호작용을 통하여 현실성과 즉시성을 유지함으로써 현실과 가상간의 실시간 동기화를 통화여 보다 정확한 예측 및 분석을 할 수 있을 것으로 기대한다.

• 한국기계가공학회지
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• 제19권11호
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• pp.42-48
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• 2020

The agricultural tractor is a multi-purpose vehicle, which is frequently used in the agricultural field. It must be highly reliable in terms of human safety. Design and analysis of agricultural tractors must be performed using actual agricultural workload to maintain high reliability. Additionally, the frequency with which various components and systems are used must also be taken into consideration. In this study, a tractor is built to measure its workload in the actual field. Further, the measured load was analyzed for various farming tasks. The range of ratios of consumed power to engine power was measured to be 42.6%-87.2%, 75.1%-97%, 26.5%-59.2% for a plow, rotary, and harvest tasks, respectively. The results were fed into a transmission simulation model to analyze the strength and life of the transmission components. We conclude that a more reliable product can be constructed by incorporating the transmission analyses using the actual load.

• 산업경영시스템학회지
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• 제44권4호
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• pp.208-219
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• 2021

In the era of the 4th industrial revolution driven by the convergence of ICT(information and communication technology) and manufacturing, research on smart factories is being actively conducted. In particular, the manufacturing industry prefers smart factories that autonomously connect and analyze data. For the efficient implementation of smart factories, it is essential to have an integrated production system that vertically integrates separately operated production equipment and heterogeneous S/W systems such as ERP, MES. In addition, it is necessary to double-verify production data by using automatic data collection technology so that the production process can be traced transparently. In this study, we want to show a case of data-centered integration of a large aircraft parts processing factory that requires high precision, takes a long time, and has the characteristics of processing large raw materials. For this, the components of the data-oriented integrated production system were identified and the connection structure between them was explained. And we would like to share the experience gained through the design and implementation case. The integrated production system proposed in this study integrates internal components based on data, which is expected to serve as a basis for SMEs to develop into an advanced stage, and traces materials with RFID technology.

• 한국기계가공학회지
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• 제20권11호
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• pp.36-42
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• 2021

This study was conducted to analyze the effect of the gear specification and gear quality corresponding to the macro geometry on the gear transmission error. The two pairs of gears with large and small transmission errors were selected for calculation, and two pairs of gears were manufactured with different gear quality. The test gears were manufactured by two different gear specifications with ISO 5 and 8 gear quality, respectively. The transmission error measurement system consists of an input motor, reducer, encoders, gearbox, torque meter, and powder brake. To confirm the repeatability of the test results, repeatability was confirmed by performing three repetitions under all conditions, and the average value was used to compare the transmission error results. The transmission errors of the gears were analyzed and compared with the test results. When the gear quality was high, the transmission error was generally low depending on the load, and the load at which the decreasing transmission error phenomenon was completed was also lower. Even when the design transmission error according to the gear specification was different, the difference of the minimum transmission error was not large. The transmission error at the load larger than the minimum transmission error load increased to a slope similar to the slope of the analysis result.

• 산업융합연구
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• 제21권1호
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• pp.103-110
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• 2023

본 연구는 우리나라 중소기업의 인더스트리 4.0의 성숙도, 클러스터링의 지표 요인, 주요 요인이 기업의 자체 평가에 미치는 영향을 알아보기 위해 클러스터링 분석을 수행하였다. 80개의 국내 중소기업을 4가지 범주로 분류하면 프로세스, 기술 및 조직 간에 유의미한 양의 상관관계가 있음을 발견하였다. 또한, 성숙도 모델에 따라 테스트된 80개 기업의 대다수는 아직 미성숙하거나 부분적으로 성숙한 것으로 보이며 인더스트리 4.0과 관련된 혁신 전략의 많은 개선과 재평가가 필요한 것으로 나타났다. 마지막으로, 싱가포르 스마트 산업 준비 지수는 국내 중소기업에서 자체 평가를 수행하는 데 적합하다는 결론을 도출하였다. 이러한 결론은 실무자와 연구자에게 유용한 성숙도 및 그룹화 지침으로 제공될 수 있다.

• Journal of Information Technology Applications and Management
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• 제27권1호
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• pp.75-95
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• 2020

Smart Factory is the decisive factor of the Fourth Industrial Revolution and is a key field for national competitiveness. Until now, most smart factory research has focused on policy and technology. In order to spread more technology, it is necessary to study what factors influence the adoption of smart factory technology in the enterprise. Nevertheless, little research has been done. In this study, based on the UTAUT (Unified Theory of Acceptance and Use of Technology), which has been proved through many years of research, I have studied the factors that influence the acceptance of smart factory technology. As a result of research, performance expectancy, social influence, and facilitating conditions of UTAUT model had a positive(+) effect on behavior intention. Their relationship of influence was in the order of performance expectancy (β = .459)> facilitating conditions (β = .212)> social influence (β = .210). However, it was found that the effort expectancy did not affect the behavior intention, and the impact of the newly perceived risk on the behavior intention to use was not confirmed. The main reason is that the acceptance of smart factory technology is not a matter of personal interest but a matter of organizational choice. Trust, on the other hand, was found to be partially mediated between performance expectancy, facilitating conditions, social influence and behavior intention. For many years, many researchers have validated the UTAUT, which has been validated through various empirical studies. It is academically meaningful to begin the study of factors affecting the acceptance of smart factory technology in terms of the UTAUT. In practice, it is necessary to provide SME employees with more information related to the introduction of smart factories, to provide advanced services related to the establishment of smart factories, and to establish a standardized model for each industry.