• The Journal of the Korea institute of electronic communication sciences
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• v.14 no.1
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• pp.199-204
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• 2019

This paper introduces smart mirror that provides the contents needed for an individual's daily life. When a command that is designated as voice recognition is entered, Smart Mirror is produced that outputs desired contents from a display. The contents of the current smart mirror include time, weather, subway information, schedule and photography. Smart mirror sold for commercial private households is difficult to distribute due to high prices, but the smart mirror production presented in this paper can lower the manufacturing cost and can be more easily used by voice recognition.

• International journal of advanced smart convergence
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• v.8 no.1
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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.

• Journal of Korean Society for Quality Management
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• v.48 no.1
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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.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.44 no.2
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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.

• Journal of Convergence for Information Technology
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• v.11 no.11
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• pp.172-177
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• 2021

As part of the digital era, a digital twin that simulates the weak part of a product by performing a stress test that reduces the lifespan of some expensive equipment that cannot be done in reality by accurately moving the real world to virtual reality is being actively used in the manufacturing industry. Due to the development of IoT, the digital twin, which accurately collects data collected from the real world and makes it the same in the virtual space, is mutually beneficial through accurate prediction of urban life problems such as traffic, disaster, housing, quarantine, energy, environment, and aging. Based on its action, it is positioned as a necessary tool for smart city construction. Although digital twin is widely applied to the manufacturing field, this study proposes a smart city model suitable for the 4th industrial revolution era by using it to smart cities and increasing citizens' safety, welfare, and convenience through the proposed model. In addition, when a digital twin is applied to a smart city, it is expected that more accurate prediction and analysis will be possible by real-time synchronization between the real and virtual by maintaining realism and immediacy through real-time interaction.

• Journal of the Korean Society of Manufacturing Process Engineers
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• v.19 no.11
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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.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.44 no.4
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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.

• Journal of the Korean Society of Manufacturing Process Engineers
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• v.20 no.11
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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.

• Journal of Industrial Convergence
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• v.21 no.1
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• pp.103-110
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• 2023

In this study, clustering analysis was performed to find out the influence of the maturity level of Industry 4.0 of SMEs in Korea, index factors of clustering, and major factors on the self-evaluation of companies. When 80 domestic SMEs were classified into 4 categories, it was found that there was a significant positive correlation between process, technology and organization. In addition, the majority of the 80 companies tested according to the maturity model appear to be immature or partially mature, and many improvements and re-evaluation of innovation strategies related to Industry 4.0 are needed. Finally, it was concluded that the Singapore Smart Industry Readiness Index is suitable for conducting self-assessment in domestic SMEs. These conclusions can serve as useful maturity and grouping guidelines for practitioners and researchers.

• Journal of Information Technology Applications and Management
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• v.27 no.1
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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.