• Journal of the Korean Society of Manufacturing Process Engineers
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• v.18 no.8
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• pp.104-109
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• 2019

Damage caused by wild animals such as pheasants and magpies is a problem in rural areas. A bird repellent system based on sensing and repelling farm pest animals and birds is proposed herein. This system is equipped with a bird model part on a supporting platform and comprises a sound source generator, a system control user interface, and a sensor in the center. The sensor is composed of an illuminance sensor and a PIR sensor. The illuminance sensor distinguishes between day and night, whereas the PIR sensor detects birds or wild animals and outputs them from the sound generator. The entire system can be managed easily by the user interface and system control.

• Journal of the Korea Safety Management & Science
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• v.26 no.1
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• pp.45-53
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• 2024

A Smart factories are systems that enable quick response to customer demands, reduce defect rates, and maximize productivity. They have evolved from manual labor-intensive processes to automation and now to cyber-physical systems with the help of information and communication technology. However, many small and medium-sized enterprises (SMEs) are still unable to implement even the initial stages of smart factories due to various environmental and economic constraints. Additionally, there is a lack of awareness and understanding of the concept of smart factories. To address this issue, the Cooperation-based Smart Factory Construction Support Project was launched. This project is a differentiated support project that provides customized programs based on the size and level of the company. Research has been conducted to analyze the impact of this project on participating and non-participating companies. The study aims to determine the effectiveness of the support policy and suggest efficient measures for improvement. Furthermore, the research aims to provide direction for future support projects to enhance the manufacturing competitiveness of SMEs. Ultimately, the goal is to improve the overall manufacturing industry and drive innovation.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.23 no.1
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• pp.163-170
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• 2023

This paper introduces the semantic modeling technology for autonomous control of manufacturing facilities and status definition algorithm. With the development of digital twin technology and various ICT technologies of the smart factory, a new production management model is being built in the manufacturing industry. Based on the advanced smart manufacturing technology, the status determination algorithm was presented as a methodology to quickly identify and respond to problems with autonomous control and facilities in the factory. But the existing status determination algorithm informs the user or administrator of error information through the grid map and is presented as a model for coping with it. However, the advancement and direction of smart manufacturing technology is diversifying into flexible production and production tailored to consumer needs. Accordingly, in this paper, a technology that can design and build a factory using a semantic-based Linked List data structure and provide only necessary information to users or managers through graph-based information is introduced to improve management efficiency. This methodology can be used as a structure suitable for flexible production and small-volume production of various types.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.27 no.6
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• pp.1467-1482
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• 2017

Recently, Interests on The Fourth Industrial Revolution has been increased. In the manufacturing sector, the introduction of Smart Factory, which automates and intelligent all stages of manufacturing based on Cyber Physical System (CPS) technology, is spreading. The complexity and uncertainty of smart factories are likely to cause unexpected problems, which can lead to manufacturing process interruptions, malfunctions, and leakage of important information to the enterprise. It is emphasized that there is a need to perform systematic management by analyzing the threats to the Smart Factory. Therefore, this paper systematically identifies the threats using the STRIDE threat modeling technique using the data flow diagram of the overall production process procedure of Smart Factory. Then, using the Attack Tree, we analyze the risks and ultimately derive a checklist. The checklist provides quantitative data that can be used for future safety verification and security guideline production of Smart Factory.

• Journal of the Korea Safety Management & Science
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• v.23 no.3
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• pp.47-55
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• 2021

Developed countries that have experienced decline in productivity due to the economic crisis in the past have come to recognize the smart factory as an important means to strengthen the competitiveness of the manufacturing industry due to the increase in labor costs, the avoidance of the manufacturing industry, and the resolution of the shortage of skilled manpower. The necessity of nurturing manpower for self-maintenance was felt through identifying factors for successful smart factory introduction by companies and providing smart factory education. Therefore, the effects of educational satisfaction and operational competency on self-efficacy as a parameter and self-efficacy as a parameter were analyzed using research models and hypotheses to determine whether there was an effect between job satisfaction as a dependent variable. As a result of the analysis, it was found that the mediating effect of self-efficacy and self-efficacy on job satisfaction was found to have significant effects on operational competency and self-efficacy as parameters, as well as educational satisfaction and operational competency. The implication of this study is that continuous education and innovation activities are important in order to increase the business performance of companies, and through this, the manufacturing competitiveness of SMEs can be improved.

• International Journal of Internet, Broadcasting and Communication
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• v.13 no.4
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• pp.121-128
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• 2021

In order to design a real time big data collection and analysis system of manufacturing data in a smart factory, it is important to establish an appropriate wired/wireless communication system and protocol. This paper introduces the latest communication protocol, OPC-UA (Open Platform Communication Unified Architecture) based client/server function, applied user interface technology to configure a network for real-time data collection through IoT Integration. Then, Database is designed in MES (Manufacturing Execution System) based on the analysis table that reflects the user's requirements among the data extracted from the new cutting process automation process, bush inner diameter indentation measurement system and tool monitoring/inspection system. In summary, big data analysis system introduced in this paper performs SPC (statistical Process Control) analysis and visualization analysis with interface of OPC-UA-based wired/wireless communication. Through AI learning modeling with XGBoost (eXtream Gradient Boosting) and LR (Linear Regression) algorithm, quality and visualization analysis is carried out the storage and connection to the cloud.

• 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.

• Proceedings of the Korean Society of Computer Information Conference
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• 2023.01a
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• pp.443-445
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• 2023

Following the global financial crisis, the global value chain contracted, and characteristics of "reverse globalization" of the economy and trade gradually emerged. This is due to the term "reverse globalization" referring to a shift away from globalization. Within a short period of time, the phenomenon known as "reverse globalization" developed as an inescapable obstacle, coinciding with the development and dissemination of the COVID-19 virus. At some time in the distant future, the "reverse globalization" of economic trade and the "globalization" of the digital economy will co-dominate the shifting trend of the global economic landscape. This will happen gradually over time. The goal of this research is to look at the minor changes that happened in the methods and techniques used by the economic mechanism known as "globalization against the flow." It employs Chinese smart manufacturing companies as a model and proposes a digital drive model to investigate the prospects and constraints of smart manufacturing technology enterprise innovation development under "reverse globalization," with the goal of establishing a digital innovation development path. The theoretical insights given in this study have the potential to serve as a reference for China as it attempts to build a new growth pattern based on a double-cycle and promote a new type of globalization.

• Journal of the Semiconductor & Display Technology
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• v.22 no.4
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• pp.108-112
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• 2023

Precision manufacturing technology is rapidly developing due to the extreme miniaturization of semiconductor processes to comply with Moore's Law. Accurate and precise alignment, which is one of the key elements of the semiconductor pre-process and post-process, is very important in the semiconductor process. The center detection of wafer align marks plays a key role in improving yield by reducing defects and research on accurate detection methods for this is necessary. Methods for accurate alignment using traditional image sensors can cause problems due to changes in image brightness and noise. To solve this problem, engineers must go directly into the line and perform maintenance work. This paper emphasizes that the development of AI technology can provide innovative solutions in the semiconductor process as high-resolution image and image processing technology also develops. This study proposes a new wafer center detection method through variable thresholding. And this study introduces a method for detecting the center that is less sensitive to the brightness of LEDs by utilizing a high-performance object detection model such as YOLOv8 without relying on existing algorithms. Through this, we aim to enable precise wafer focus detection using artificial intelligence.

• Informatization Policy
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• v.25 no.3
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• pp.3-28
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• 2018

The purpose of this study is to analyze the research trends and present future research directions in the field of Cyber Physical System (CPS), a key element in the 4th Industrial Revolution, Industry 4.0, and Smart Manufacturing that are currently promoted as important innovation agenda both at home and abroad. In this study, (1) the concepts of industry 4.0, smart manufacturing and CPS are summarized; (2) analysis criteria of these fields are established; and 3) analysis results are presented and future research direction is proposed. 74 overseas and 8 domestic literature on manufacturing CPS from 2013 to 2017 are identified through 'Google Scholar Search'. Major results of the analysis are summarized as follows: (1) research on a common methodology and framework for the manufacturing CPS needs to be done based on the analysis of the existing methodologies and frameworks of various perspectives; (2) in order to improve the maturity of the manufacturing CPS, it is necessary to study actual deployment and operations of CPS, including the existing systems; (3) it is necessary to study the diagnostic methodology that can evaluate manufacturing CPS and suggest improvement strategy; and (4) as for the detailed model and tool, it is necessary to reinforce research on SCM production planning and human-machine collaboration while considering the characteristics of CPS.