• Journal of Korea Society of Industrial Information Systems
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• v.27 no.4
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• pp.65-79
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• 2022

This study identified smart manufacturing technologies using patent and topic modeling, and compared the technology development trends in countries such as the United States, Japan, Germany, China, and South Korea. To this purpose, this study collected patents in the United States and Europe between 1991 and 2020, processed patent abstracts, and identified topics by applying latent Dirichlet allocation model to the data. As a result, technologies related to smart manufacturing are divided into seven categories. At a global level, it was found that the proportion of patents in 'data processing system' and 'thermal/fluid management' technologies is increasing. Considering the fact that South Korea has relative competitiveness in thermal/fluid management technologies related to smart manufacturing, it would be a successful strategy for South Korea to promote smart manufacturing in heavy and chemical industry. This study is significant in that it overcomes the limitations of quantitative technology level evaluation proposed a new methodology that applies text mining.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.21 no.2
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• pp.41-47
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• 2021

Manufacturing is facing radical changes around the world. The manufacturing industry, which has been changing since Germany, is now being introduced, improved, and developed worldwide by manufacturers under the name of smart factory. By utilizing IT technologies such as artificial intelligence and cloud at the production site, the desire to break away from the past manufacturing environment is increasing. How these technologies will be efficient in the future, manufacturing worldwide now faces radical changes. The manufacturing industry, which has been changing since Germany, is now being introduced, improved, and developed worldwide by manufacturers under the name of smart factory. By utilizing IT technologies such as artificial intelligence and cloud at the production site, the desire to break away from the past manufacturing environment is increasing. Discussions continue on how these technologies can be used efficiently and effectively. Increasingly, the expansion of the range from factory areas to regions, countries, and around the world raises the need for international standards for interactions. In this paper, we propose a design and implementation method for managing facilities, sensors, etc. as assets and monitoring facility data collected through OPC UA.

• Journal of Venture Innovation
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• v.5 no.4
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• pp.109-115
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• 2022

As the 4th industrial revolution progresses, foreign countries are promoting smart manufacturing innovation through digital transformation as a priority task early on to secure a competitive edge in the manufacturing industry. In response, the Korean government is also promoting a policy to enhance the competitiveness of small and medium-sized manufacturing companies by promoting digital transformation in the corporate sector to meet the global trend of the 4th industrial revolution era. Manufacturing powerhouses such as Germany and Japan see manufacturing as a key sector in digital transformation and are leading related policies, while emerging countries such as China are also promoting manufacturing innovation strategies such as building digital infrastructure and creating a digital innovation ecosystem. Korea is promoting the 'Korean-style smart factory dissemination and expansion strategy' by transforming Germany's manufacturing innovation strategy for smart factory supply to suit the domestic situation. However, the policy to supply smart factories so far has been conducted with support from individual companies under the leadership of the government, and most of the smart factories are at the basic level, and it is evaluated that there are limitations such as the lack of manpower to operate smart factories. In addition, while the current policy focuses on expanding the supply of smart factories in SMEs, it is necessary to establish a smart manufacturing system through linkages between large and small businesses in order to achieve the original goal of establishing a smart manufacturing system. Therefore, it can be said that from the standpoint of small and medium-sized enterprises (SMEs), who are consumers of smart factories, it can be said that the digital transformation policy can achieve the expected results only when appropriate incentives are provided for the introduction of smart factories in a situation where management resources such as funds, technology, and human resources are lacking. In addition, it is judged that the uncertainty of the performance of digital investment always exists, and as long as large and small companies are maintained as an ecosystem of delivery and subcontracting, there is very little incentive for small and medium-sized manufacturing companies to voluntarily invest in or advance digital transformation. Therefore, the digital transformation policy of small and medium-sized manufacturing companies in the future has practical significance in that it suggests that there is a need to seek ways to attract SMEs' digital-related voluntary investment.

• The Journal of Korea Robotics Society
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• v.18 no.2
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• pp.225-232
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• 2023

With the recent increase of multiple robots cooperating in smart manufacturing logistics environments, it has become very important how to predict the safety and efficiency of the individual tasks and dynamically assign them to the best one of available robots. In this paper, we propose a novel task policy learner based on deep relational reinforcement learning for predicting the safety and efficiency of tasks in a multi-robot manufacturing logistics environment. To reduce learning complexity, the proposed system divides the entire safety/efficiency prediction process into two distinct steps: the policy parameter estimation and the rule-based policy inference. It also makes full use of domain-specific knowledge for policy rule learning. Through experiments conducted with virtual dynamic manufacturing logistics environments using NVIDIA's Isaac simulator, we show the effectiveness and superiority of the proposed system.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.42 no.3
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• pp.25-38
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• 2019

This study is to develop a diagnostic model for the effective introduction of smart factories in the manufacturing industry, to diagnose SMEs that have difficulties in building their own smart factory compared to large enterprise, to identify the current level and to present directions for implementation. IT, AT, and OT experts diagnosed 18 SMEs using the "Smart Factory Capacity Diagnosis Tool" developed for smart factory level assessment of companies. They analyzed the results and assessed the level by smart factory diagnosis categories. Companies' smart factory diagnostic mean score is 322 out of 1000 points, between 1 level (check) and 2 level (monitoring). According to diagnosis category, Factory Field Basic, R&D, Production/Logistics/Quality Control, Supply Chain Management and Reference Information Standardization are high but Strategy, Facility Automation, Equipment Control, Data/Information System and Effect Analysis are low. There was little difference in smart factory level depending on whether IT system was built or not. Also, Companies with large sales amount were not necessarily advantageous to smart factories. This study will help SMEs who are interested in smart factory. In order to build smart factory, it is necessary to analyze the market trends, SW/ICT and establish a smart factory strategy suitable for the company considering the characteristics of industry and business environment.

• Journal of information and communication convergence engineering
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• v.18 no.1
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• pp.61-68
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• 2020

In the era of the fourth industrial revolution, many production plants are gradually evolving into smart factories that apply information and communication technology to manufacturing, distribution, production, and quality management. The conversion from conventional factories to smart factories has resulted in the automation of production sites using the internet and the internet of things (IoT) technology. Thus, labor-intensive production can easily collect necessary information. However, implementing a smart factory required a significant amount of time, effort, and money. In particular, labor-intensive production industries are not automated, and productivity is determined by human skill. A representative industry of such industries is sewing the industry. In the sewing industry, wherein productivity is determined by the operator's skills. This study suggests that production performance, inventory management and product delivery of the sewing industries can be managed efficiently with existing production method by using smart buttons incorporating IoT functions, without using automated machinery.

• Journal of the Korean Society of Systems Engineering
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• v.11 no.2
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• pp.41-49
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• 2015

Industrial safety is one of the crucial agenda for Government as well as Manufacturing Industry. To cope with the needs, a great deal of policies and technical implementation have been proposed and implemented. With a great increasing attention on the Industry 4.0 and Smart Factory, industrial safety has received as a crucial agenda by the manufacturing industry in particular. Up until now, almost all of them have been made from the environmental aspects, rather than operator or workers. In this paper, we present our research results how to increase the workers' safety via smart factory technology, such as IoT and CPS. Our approach has been to see the problem from SE perspectives, to draw the real issues from the various stakeholders, and define how to solve the problem based on the emerging technologies. The developed systems can give conceptual framework for the 'smart' industrial safety system by providing solution architecture for how to monitor the location of workers, and moving equipments, and generate solutions how to avoid safety problems between them if detected.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2021.05a
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• pp.305-307
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• 2021

In this paper, we propose a study related to the development of a Digital-Twin platform using a smart factory based CPPS (Cyber Pysical Production System) using ICT (Information Communication Technology) technology. The platform developed through this study performs a 3D model simulation function in conjunction with P3R (Product, Process, Plant, Resource) including BOP (Bill of Process) management function from the preceding manufacturing process planning stage. In addition, we propose a digital twin platform that can predict production processes, equipment, layout, and production. The platform proposed through this paper proposes a feature that can manage the entire smart factory manufacturing process from the initial planning design stage to the manufacturing, production, operation, and maintenance stages.

• Korean small business review
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• v.42 no.2
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• pp.117-137
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• 2020

We examine the current status of smart factory deployment and diffusion programs in Korea, and seek to promote manufacturing innovation from the perspective of SMEs. The main conclusions of this paper are as follows. First, without additional market creation and supply chain improvement, smart factories are unlikely to raise profitability leading to overinvestment. Second, new business models need to connect "manufacturing process efficiency" with "R&D" and "marketing" in value chain in smart factories. Third, when introducing smart factories, we need to focus on the areas where process-embedded technology is directly linked to corporate competitiveness. Based on the modularity-maturity matrix (Pisano and Shih, 2012) and the examples of U.S. Manufacturing Innovation Institute (MII), we establish the new smart factory deployment policy measures as follows. First, we shift our smart factory strategy from quantitative expansion to qualitative upgrading. Second, we promote by each sector the formation of industrial commons that help SMEs to jointly develop R&D, exchange standardized data and practices, and facilitate supplier-led procurement system. Third, to implement new technology and business models, we encourage partnerships, collaborations, and M&As between conventional SMEs and start-ups and business ventures. Fourth, the whole deployment process of smart factories is indexed in detail to identify the problems and provide appropriate solutions.

• Journal of Practical Engineering Education
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• v.11 no.2
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• pp.283-289
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• 2019

Recently, product mobility, data compatibility and communication connectivity have become very important to the control system, depending on the application of smart manufacturing. Accordingly, embedded systems are essential in all industries including home appliances, telecommunication, and national defense. Therefore, the demand for embedded system development personnel is increasing further, and education and training programs are needed to combine the practical skills of industrial sites, including programming skills and hardware. Currently, embedded system education offers a variety of education centered on Aduino, but this is mostly for beginners and is not sufficient for majors. In addition, while various prototype studies related to embedded systems are active, the training and training programs for working-level human resources needed at industrial sites are very scarce. Therefore, in order to foster the working personnel of the embedded system for the application of smart manufacturing, this paper selected the capability unit through in-depth interviews and survey analysis of 10 experts based on NCS, and developed education and training programs and contents.