• Journal of the Korean Society of Manufacturing Process Engineers
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• v.5 no.4
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• pp.79-85
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• 2006

Multi-filament hydrostatic extrusion was developed as a fine wire manufacturing process and wire forming experiments were conducted. Also, single wire stretch forming process was proposed in the possibility of obtaining long wire with constant cross-section. In the multi filament extrusion since the workpiece, die and forming facility are in the macro forming circumstance, fine wire and fine hole structure with less than a few micrometer can be easily obtained. Although stretch forming does not use a die in order to avoid the friction problem between the workpiece and the die, it is necessary to have high level of technology to maintain cross-sectional shape and measure in longitudinal direction.

• Journal of the Korea Safety Management & Science
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• v.19 no.1
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• pp.227-236
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• 2017

Smart Manufacturing Factory is a paradigm of the future lead to the fourth industrial revolution that led Germany and the United States. Now the automation of the production facility and won a certain degree, and through the process of integrating the entire process, including planning, design, distribution of information and communication technology products in emerging as a core competitiveness of the national economy. In particular, the company accelerated the smart factory building in order to improve the manufacturing industry, cost savings and productivity simply to incorporate internet of things(IoT),Robot, artificial intelligence, big data technology as a factory automation level of sophistication of the system and out to progress to the level that replaces human labor have. In this we should look at the trend of promoting domestic and foreign factories want to present these smart strategies for Korea.

• Nuclear Engineering and Technology
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• v.54 no.1
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• pp.244-254
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• 2022

For tensile tests, Vickers hardness tests and microstructure tests, plate-type and box-type specimens of austenitic 316L stainless steels were produced by a conventional machining (CM) process as well as two additive manufacturing processes such as direct metal laser sintering (DMLS) and direct metal tooling (DMT). The specimens were irradiated up to a fast neutron fluence of 3.3 × 10⁹ n/cm² at a neutron irradiation facility. Mechanical performance of the unirradiated and irradiated specimens were investigated at room temperature and 300 ℃, respectively. The tensile strengths of the DMLS, DMT and CM 316L specimens are in descending order but the elongations are in reverse order, regardless of irradiation and temperature. The ratio of Vickers hardness to ultimate tensile strength was derived to be between 3.21 and 4.01. The additive manufacturing processes exhibit suitable mechanical performance, comparing the tensile strengths and elongations of the conventional machining process.

• Journal of Korea Multimedia Society
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• v.25 no.8
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• pp.1085-1096
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• 2022

The manufacturing industry is continuously pursuing advanced technology and smartization as it converges with innovative technology. Improvement of manufacturing productivity is achieved by monitoring, analyzing, and controlling the facilities and processes of the manufacturing site in real time through a network. In this paper, we proposed a new OPC-UA based digital twin model for smart factory facilities. A testbed system for USB flash drive packaging facility was implemented based on the proposed digital twin model and OPC-UA data communication scheme. Through OPC-UA based digital twin model, equipment and process status information is transmitted and received from PLC to monitoring and control 3D digital models and physical models in real time. The usefulness of the developed digital twin testbed system was evaluated through usability test.

• Electronics and Telecommunications Trends
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• v.35 no.1
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• pp.60-70
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• 2020

Artificial intelligence (AI) is expected to bring about a wide range of changes in the industry, based on the assessment that it is the most innovative technology in the last three decades. The manufacturing field is an area in which various artificial intelligence technologies are being applied, and through accumulated data analysis, an optimal operation method can be presented to improve the productivity of manufacturing processes. In addition, AI technologies are being used throughout all areas of manufacturing, including product design, engineering, improvement of working environments, detection of anomalies in facilities, and quality control. This makes it possible to easily design and engineer products with a fast pace and provides an efficient working and training environment for workers. Also, abnormal situations related to quality deterioration can be identified, and autonomous operation of facilities without human intervention is made possible. In this paper, AI technologies used in smart factories, such as the trends in generative product design, smart workbench and real-sense interaction guide technology for work and training, anomaly detection technology for quality control, and intelligent manufacturing facility technology for autonomous production, are analyzed.

• Journal of the Korean Society of Manufacturing Technology Engineers
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• v.24 no.4
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• pp.463-468
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• 2015

High vibration of a low pressure (LP) turbine casing caused safety problems and life at the facility it was housed in. The main focus of this study was the cause of the high vibration in a low pressure turbine casing with manufacturing defects by frequency response analysis, compared with the results of experiments. Therefore, excited accelerations were obtained from the LP casing fundamental, and frequency responses were analyzed. The measurement and the modal analysis showed that the natural frequency of the LP turbine casing was 61.26 Hz and the excited frequency of the turbine rotor was 60.25 Hz. The manufacturing defect caused a decrease in the casing natural frequency and resulted in the high vibration of the casing because it moved close to the resonant frequency.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.17 no.8
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• pp.38-45
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• 2016

The objective of this research is to develop a method of improving MEP facility management based on 3D reverse engineering. Recently, 3D image scanning-based reverse engineering has been implemented in the fields of architecture, construction and (manufacturing). In the case where there are many objects and the MEP system is complicated, 3D reverse engineering is applied in semiconductor factories, because facility maintenance works cause the 2D drawing to be different from the original one. The 3D point cloud data obtained from 3D image scanning contains accurate data and can increase the efficiency of complicated MEP facility maintenance works. For this purpose, the present research studied the technology trends and analyzed the process of 3D reverse engineering. Based on the results, a method of improving MEP facility management is established and its effects described.

• The KIPS Transactions:PartD
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• v.15D no.2
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• pp.263-270
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• 2008

Research of ubiquitous computing becomes more popular topic along with the rapid development of wireless technologies. Firstly, research and development on RFID focuses on manufacturing and retail sectors, because it can improve supply chain efficiency. But, it changes to USN (Ubiquitous Sensor Network) by adding a sensor and wireless network technologies on it. In this research, we design and implement the electric facility management service framework to collect real time information of electric facility using RFID/USN. In electric power industry, it is important the supply of energy must be guaranteed. So many power utilities control and supervise the transmission line to avoid power failures. Utilities install many types of sensor to monitor important facilities by wired network such as optical cable and PLC. In this research, we develop the sensor node which is small, easy to install and using wired network. We design the service framework for electric facility management to collect data using RFID tag, reader and wireless sensor nodes and implement the electric facility management service.

• Journal of Internet Computing and Services
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• v.19 no.6
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• pp.21-29
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• 2018

In the era of the 4-th industrial revolution, the concept of smart factory is emerging. There are efforts to predict the occurrences of facility errors which have negative effects on the utilization and productivity by using data analysis. Data composed of the situation of a facility error and the type of the error, called the facility error log, is required for the prediction. However, in many manufacturing companies, the types of facility error are not precisely defined and categorized. The worker who operates the facilities writes the type of facility error in the form with unstructured text based on his or her empirical judgement. That makes it impossible to analyze data. Therefore, this paper proposes a framework for constructing a phrase network to support the identification and classification of facility error types by using facility error logs written by operators. Specifically, phrase indicating the types are extracted from text data by using dictionary which classifies terms by their usage. Then, a phrase network is constructed by calculating the similarity between the extracted phrase. The performance of the proposed method was evaluated by using real-world facility error logs. It is expected that the proposed method will contribute to the accurate identification of error types and to the prediction of facility errors.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.39 no.3
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• pp.180-191
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• 2016

Optimizing energy usage for maximum efficiency is an essential goal for manufacturing plants in every industrial manufacturing sector. The generation and distribution of purifying compressed air is a large expense incurred in practically all manufacturing processes. Not only is the generation and treatment expensive equipment of compressed air, but frequent maintenance and effective operation is also required. As a plant's compressed air system is often an integral part of the production process, it needs to be reliable, efficient, and easy to be maintain. In this paper, we study to find operating method to save energy from the adsorption dryer in the process of purifying compressed air, which is required for a clean room production site in "A" company. The compressed air passes through a pressure vessel with two "towers" filled with a material such as activated alumina, silica gel, molecular sieve or other desiccant material. This desiccant material attracts the water from the compressed air via adsorption. As the water clings to the desiccant, the desiccant particle becomes saturated. Therefore, Adsorption dryer is an extremely significant facility which removes the moisture in the air $70^{\circ}C$ below the dew point temperature while using a lot of energy. Also, the energy consumption of the adsorption dryer can be varied by various operating conditions (time, pressure, temperature, etc). Therefore, based on existing operating experiments, we have searched operating condition to maximize energy saving by changing operating conditions of the facility. However, due to a short experiment period (from September to October), further research will be focused on considering seasonality.