• Journal of Korea Multimedia Society
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• v.19 no.8
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• pp.1516-1529
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• 2016

While global manufacturing is becoming more competitive due to variety of customer demand, increase in production cost and uncertainty in resource availability, the future ability of manufacturing industries depends upon the implementation of Smart Factory. With the convergence of new information and communication technology, Smart Factory enables manufacturers to respond quickly to customer demand and minimize resource usage while maximizing productivity performance. This paper presents the development of a big data analytics platform architecture for Smart Factory. As this platform represents a conceptual software structure needed to implement data-driven decision-making mechanism in shop floors, it enables the creation and use of diagnosis, prediction and optimization models through the use of data analytics and big data. The completion of implementing the platform will help manufacturers: 1) acquire an advanced technology towards manufacturing intelligence, 2) implement a cost-effective analytics environment through the use of standardized data interfaces and open-source solutions, 3) obtain a technical reference for time-efficiently implementing an analytics modeling environment, and 4) eventually improve productivity performance in manufacturing systems. This paper also presents a technical architecture for big data infrastructure, which we are implementing, and a case study to demonstrate energy-predictive analytics in a machine tool system.

• Journal of Convergence for Information Technology
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• v.8 no.2
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• pp.233-238
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• 2018

SMEs and small enterprises are making various attempts to manage SMEs in terms of equipment, safety and energy management as well as production management. However, SMEs do not have the investment capacity and it is not easy to build a smart factory to improve management and productivity of SMEs. In this paper, we propose a smart factory construction algorithm that partially integrates the factory equipment currently operated by SMEs. The proposed algorithm supports collection, storage, management and processing of product information and release information through IoT device during the whole manufacturing process so that SMEs' smart factory environment can be constructed and operated in stages. In addition, the proposed algorithm is characterized in that central server manages authentication information between devices to automate the linkage between IoT devices regardless of the number of IoT devices. As a result of the performance evaluation, the proposed algorithm obtained 13.7% improvement in the factory process and efficiency before building the Smart Factory environment, and 19.8% improvement in the processing time in the factory. Also, the cost of input of manpower into process process was reduced by 37.1%.

• Journal of the Korea Society of Computer and Information
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• v.24 no.5
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• pp.49-58
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• 2019

In this paper, we propose an edge cloud platform architecture for implementing smart factory. The edge cloud platform is one of edge computing architecture which is mainly focusing on the efficient computing between IoT devices and central cloud. So far, edge computing has put emphasis on reducing latency, bandwidth and computing cost in areas like smart homes and self-driving cars. On the other hand, in this paper, we suggest not only common functional architecture of edge system but also light weight cloud based architecture to apply to the specialized requirements of smart factory. Cloud based edge architecture has many advantages in terms of scalability and reliability of resources and operation of various independent edge functions compare to typical edge system architecture. To make sure the availability of edge cloud platform in smart factory, we also analyze requirements of smart factory edge. We redefine requirements from a 4M1E(man, machine, material, method, element) perspective which are essentially needed to be digitalized and intelligent for physical operation of smart factory. Based on these requirements, we suggest layered(IoT Gateway, Edge Cloud, Central Cloud) application and data architecture. we also propose edge cloud platform architecture using lightweight container virtualization technology. Finally, we validate its implementation effects with case study. we apply proposed edge cloud architecture to the real manufacturing process and compare to existing equipment engineering system. As a result, we prove that the response performance of the proposed approach was improved by 84 to 92% better than existing method.

• Proceedings of the Korean Society for Agricultural Machinery Conference
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• 2000.11c
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• pp.607-612
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• 2000

Microprecision agriculture for a fully controlled plant factory is proposed in this paper. Microprecision agriculture can be attained by using plant factories to realize profitable alternative agriculture. A closed, fully controlled, plant-growing factory is far better in terms of minimizing all sorts of waste. The limit and optimum design concept has to be applied to establish an economically feasible, fully controlled, plant-growing factory. To achieve this objective, microprecision technologies have to be developed. Microprecision technologies should be involved in sensing, modeling, controlling, and collecting information for the mechatronics for plant production. Basic technologies for microprecision are already available; they are SPA (speaking plant approach to environmental control), AI (artificial intelligence: expert systems, neural networks, genetic algorithms, photosynthetic algorithms etc.), bioinstrumentation, non-invasive measurement, biomechatronics, and biorobotics. A microprecision irrigation system for plug production is an example of a microprecision technology that has actually been implemented in a plug seedling production factory.

• Transactions of the Korean Society of Automotive Engineers
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• v.11 no.6
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• pp.118-126
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• 2003

Digital Manufacturing is a technology facilitating effective developments and agile productions of the product via digital computer models representing physical and logical schema and the behavior of the real manufacturing systems including manufacturing resources, environments and products. For the successful application of this technology, a digital factory as a well-designed and an integrated environment is essential. In this paper, we constructed the sophisticated digital factory of a Korean automotive company's body shop, and conducted precise simulations of unit cell, lines and the whole factory for the collision check, the production flow analysis and the off-line programming. We expect that this digital factory of the body shop helps us achieve great savings in time and cost for many manufacturing preparation activities of the new car development.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2018.05a
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• pp.503-506
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• 2018

The concern about Smart Factory has increased according to the 4th revolution. The number of security threats targeting Smart Factory devices has increased over the last years and it is possible to cause the vulnerability of security about industry secret data. In this paper, we devide security requirements into four and analyze security vulnerability of Smart Factory devices and describe the attack type newly happened.

• Transactions of the Korean Society of Machine Tool Engineers
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• v.17 no.5
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• pp.64-69
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• 2008

In recent years, most of the researchers have been working on micro system manufacturing technological environment. With this international trend and one of the key researches in Korea, this paper gives the keynote on manufacturing the micro-scaled part with digital micro factory and its simulation. In order to construct and estimate reconfigurable simulation time, the digital simulation has been performed for the micro factory and for ultra small machines. From simulation result we came to know that micro factory requires less work-in space and processing time to manufacture micro-scaled part with different environment.

• Journal of Institute of Convergence Technology
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• v.7 no.2
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• pp.21-23
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• 2017

Smart Factories could be regarded as a result of the integration of various key technologies of the fourth industrial revolutions. In smart factory, the IoT (Internet of things) is applied to capture the data generated by the production facility, store and analyze data generated in real time using Big Data technology. In addition, 3D printers are used to print expensive and complex parts, industrial robots supply materials and parts to the production site, store finished products in warehouses. In this paper, we introduced the definition of smart factory and change of job market. Also, we summarize several national policies to support enhancing transformation process of smart factory.

• Journal of the Korean Society of Marine Environment & Safety
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• v.19 no.3
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• pp.277-284
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• 2013

To ensure that the production system of a factory is efficient, the factory layout design should consider the location and material flow plans of facilities, workshops, and storage areas. Highly productive factories need to have an optimized layout planning process, and a customized design methodology of the production system is a necessity for feasible layout planning. This paper presents a method for designing a layout module's size and shape and provides a heuristic location-allocation algorithm for the modules. The method is implemented and validated using a rich internet application-based platform. The layout design method is based on the leisure ship production process; this method can be used for designing the layout of a new factory or remodeling an existing factory and its production system. In contrast to existing layout methods, the inputs required for the proposed method, such as target products, production processes, and human-resource plans, are simple. This layout design method provides a useful solution for the initial stage of factory design.

• Proceedings of the Korea Society for Industrial Systems Conference
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• 1999.12a
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• pp.739-745
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• 1999

This paper forcuses on the measurement of increased work efficiency expected from the factory automation through random interactions of the organizational behavioral factors whose attributes can be changed with the implementation of the factory automations. Specifically the work reported here is concerned with modeling and analyzing the random interrelationships among the organizational behavioral factory which factory automation will have impact on throughout the time horizon of its implementation in terms of productivity. In addition, it is also concerned with developing a stochastic continuous simulation model to be used to assess the impact of factory automations.