• Journal of the Korean Society of Systems Engineering
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• 제11권2호
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• pp.95-105
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• 2015

Manufacturing Execution System(MES) is in charge of manufacturing execution in the shop floor based on the inputs given by high level information such as ERP, etc. The typical MES implemented is not tightly interconnected with shop floor control system including real (or near real) time monitoring and control devices such as PLC. The lack of real-time interfaces is one of the major obstacles to achieve accurate and optimization of the total performance index of the shop floor system. Smart factory system in the paradigm of Industry 4.0 tries to solve the problems via CPS (Cyber Physical System) technology and FILS (Factory In-the-Loop System). In this paper, we conducted Systems Engineering Approach to design an advanced MES (namely Smart MES) that can accommodate CPS and FILS concept. Specifically, we tailored Systems Engineering Process (SEP) based on an International Standard formalized as ISO/IEC 15288 to develop Stakeholders' Requirements (StR), System Requirements (SyR). The deliverables of each process are modeled and represented by the SysML, UML customized to Systems Engineering. The results of the research can provide a conceptual framework for future MES that can play a crucial role in the Smart Factory.

• Informatization Policy
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• 제25권3호
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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.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 한국정보통신학회 2022년도 추계학술대회
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• pp.204-206
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• 2022

Digital twin, which is evaluated as the core of smart factory advancement, is a technology that implements a digital replica in the virtual world with the same properties and functions of assets in the real world. Since the smart factory to which digital twin is applied can support services such as real-time production process monitoring, production process simulation, and predictive maintenance of facilities, it is expected to contribute to reducing production costs and improving productivity. AAS (Asset Administration Shell) is an essential technology for implementing digital twin and supports a method to digitally represent physical assets in real world. In this paper, we design AAS for manufacturing data gathering to be used in real-time CNC (Computer Numerical Control) monitoring system in operation by considering manufacturing facility in smart factory as assets.

• Journal of Korean Society of Industrial and Systems Engineering
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• 제44권4호
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• pp.12-22
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• 2021

This article suggests the machine learning model, i.e., classifier, for predicting the production quality of free-machining 303-series stainless steel(STS303) small rolling wire rods according to the operating condition of the manufacturing process. For the development of the classifier, manufacturing data for 37 operating variables were collected from the manufacturing execution system(MES) of Company S, and the 12 types of derived variables were generated based on literature review and interviews with field experts. This research was performed with data preprocessing, exploratory data analysis, feature selection, machine learning modeling, and the evaluation of alternative models. In the preprocessing stage, missing values and outliers are removed, and oversampling using SMOTE(Synthetic oversampling technique) to resolve data imbalance. Features are selected by variable importance of LASSO(Least absolute shrinkage and selection operator) regression, extreme gradient boosting(XGBoost), and random forest models. Finally, logistic regression, support vector machine(SVM), random forest, and XGBoost are developed as a classifier to predict the adequate or defective products with new operating conditions. The optimal hyper-parameters for each model are investigated by the grid search and random search methods based on k-fold cross-validation. As a result of the experiment, XGBoost showed relatively high predictive performance compared to other models with an accuracy of 0.9929, specificity of 0.9372, F₁-score of 0.9963, and logarithmic loss of 0.0209. The classifier developed in this study is expected to improve productivity by enabling effective management of the manufacturing process for the STS303 small rolling wire rods.

• International Journal of Advanced Culture Technology
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• 제9권4호
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• pp.355-362
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• 2021

Through the Korean version of the New Deal 2.0, manufacturing-oriented SMEs are facing a new environmental change called smart factory construction. In addition, SMEs are facing new security threats along with a contactless environment due to COVID-19. However, it is practically impossible to apply the previously researched and developed security management system to protect the core technology of manufacturing-oriented SMEs due to the lack of economic capacity of SMEs. Therefore, through research on security management systems suitable for SMEs, it is necessary to strengthen their business competitiveness and ensure sustainability through proactive responses to security threats faced by SMEs. The security management system presented in this study is a security management system to prevent technology leakage applicable to SMEs by deriving and reflecting the minimum security requirements in consideration of technology protection point of view, smart factory, and remote access in a non-contact environment. It is also designed in a modular form. The proposed security management system is standardized and can be selectively used by SMEs.

• Journal of Sensor Science and Technology
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• 제24권5호
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• pp.299-305
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• 2015

Since the natural disasters such as floods, droughts, heat wave and cold wave are increasing, the need for risk management is necessary to minimize the damage with utilizing IT technology. Also, the monitoring services of disaster response type have been developed and applied. Recently, the open source hardware based on the signal of the sensor, or the monitoring studies have been carried. In this paper, by analyzing a low-cost open source hardware platform such as Beagle board, we examine the utilization of the hardware-based module for sensor monitoring.

• Journal of the Korea Safety Management & Science
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• 제24권2호
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• pp.135-142
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• 2022

The smart factory is an important system that can reduce defects, maximize productivity, and respond to customer needs, from the labor-intensive era of traditional small and medium-sized manufacturing companies through the automation era to CPS using ICT. However, small and medium-sized manufacturers often fall short of the basic stage due to economic and environmental constraints, and there are many companies that do not even recognize the concept of a smart factory. In this situation, to expand the smart factory of small and medium-sized enterprises, the project to support the establishment of a smart factory for the win-win between large and small enterprises. The win-win smart factory construction support project provides a customized differentiation program support project according to the size and level of the company for all domestic manufacturing SMEs regardless of whether or not they are dealing with Samsung. In this study, we analyze the construction status and introduction performance of companies participating in the win-win smart factory support project to find out whether they have been helpful in management and to find efficient ways to improve support policies, and to suggest the direction of continuous support projects to improve the manufacturing competitiveness of SMEs in the future.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 한국정밀공학회 2012년도 춘계학술대회 논문집
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• pp.541-542
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• 2012

• Journal of the Semiconductor & Display Technology
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• 제22권4호
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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.

• Journal of the Korea Society of Computer and Information
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• 제25권7호
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• pp.143-150
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• 2020

Recently, many companies are interested in smart factory services. Because various smart factory services are provided by the combination of mobile devices, cloud computing, and IoT services. However, many workers turn away from these systems because most of them are not implemented from the worker's point of view. To solve this, we implemented a development tool that allows field workers to produce their own services so that workers can easily create smart factory services. Manufacturing data is collected in real time from sensors which are connected to manufacturing facilities and stored within smart factory platforms. Implemented development tools can produce services such as monitoring, processing, analysis, and control of manufacturing data in drag-and-drop. The implemented system is effective for small manufacturing companies because of their environment: making various services quickly according to the company's purpose. In addition, it is assumed that this also will help workers' improve operation skills on running smart factories and fostering smart factory capable personnel.