• KOREAN JOURNAL OF PACKAGING SCIENCE & TECHNOLOGY
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• v.29 no.3
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• pp.203-209
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• 2023

The increasing saturation challenges in storage facilities for Low- and Intermediate-Level Radioactive Waste call for a more efficient storage approach. Consequently, we have developed a square-structured container that features a storage capacity approximately 20% greater than that of conventional drum-type containers. Considering the need to contain various radioactive wastes from nuclear power usage securely until they no longer pose a threat to human health or the environment, this study focuses on evaluating the sealing efficacy of the newly designed rectangular container using finite element analysis. Since radioactive waste containers typically do not experience external forces except under special circumstances, our analysis simulated the impact of an external force, assuming a fall scenario. After fastening the bolts, we examined the vertical stress distribution on the container by applying the calculated external force. The analysis confirms the container's stable seal.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.26 no.2
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• pp.199-206
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• 2022

A malfunction or breakdown of a manufacturing facility leads to product defects and the suspension of production lines, resulting in huge financial losses for manufacturers. Due to the spread of smart factory services, a large amount of data is being collected in factories, and AI-based research is being conducted to predict and diagnose manufacturing facility breakdowns or manufacturing site efficiency. However, because of the characteristics of manufacturing data, such as a severe class imbalance about abnormalities and ambiguous label information that distinguishes abnormalities, developing classification or anomaly detection models is highly difficult. In this paper, we present an deep learning algorithm for anomaly detection of a manufacturing facility using reconstruction loss of CNN-based model and ananlyze its performance. The algorithm detects anomalies by relying solely on normal data from the facility's manufacturing data in the exclusion of abnormal data.

• Information Systems Review
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• v.22 no.4
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• pp.185-203
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• 2020

With the advent of 4th industrial revolution, the manufacturing industry is converging with ICT and changing into the era of smart manufacturing. In the smart factory, all machines and facilities are connected based on ICT, and thus security should be further strengthened as it is exposed to complex security threats that were not previously recognized. To reduce the risk of security incidents and successfully implement smart factories, it is necessary to identify key security factors to be applied, taking into account the characteristics of the industrial environment of smart factories utilizing ICT. In this study, we propose a 'hierarchical classification model of security factors in smart factory' that includes terminal, network, platform/service categories and analyze the importance of security factors to be applied when developing smart factories. We conducted an assessment of importance of security factors to the groups of smart factories and security experts. In this study, the relative importance of security factors of smart factory was derived by using AHP technique, and the priority among the security factors is presented. Based on the results of this research, it contributes to building the smart factory more securely and establishing information security required in the era of smart manufacturing.

• Journal of Korea Society of Industrial Information Systems
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• v.21 no.2
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• pp.1-10
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• 2016

In this paper, we consider the algorithms and numerical analysis for real-time integrated control system and resource management of large-scale manufacturing smart factory system. There various data transmitted on Cyber-Physical-System (CPS) is necessary to control in real time, as well as the terminal and the platform with respective system service. This will be a true smart manufacturing which consisting of existing research results, and a numerical analysis by the parameter-specific information. In this paper, Jacoby calculation to reflect the optimization algorithms that are newly proposed. It also presents a behavior that optimal operational algorithm on CPS which is adapted to the sensing data. In addition, we also verify the excellence of the real-time integrated control system through experimentation, by comparison with the existing research results.

• Journal of Convergence for Information Technology
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• v.9 no.3
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• pp.16-21
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• 2019

Small and medium-sized Korean companies are currently changing their industrial structure faster than in the past due to various environmental factors (such as securing competitiveness and developing excellent products). In particular, the importance of collecting and utilizing data produced in smart factory environments is increasing as diverse devices related to artificial intelligence are put into manufacturing sites. This paper proposes an artificial intelligence-based smart factory model to improve the process of products produced at the manufacturing site with the recent smart factory. The proposed model aims to ensure the increasingly competitive manufacturing environment and minimize production costs. The proposed model is managed by considering not only information on products produced at the site of smart factory based on artificial intelligence, but also labour force consumed in the production of products, working hours and operating plant machinery. In addition, data produced in the proposed model can be linked with similar companies and share information, enabling strategic cooperation between enterprises in manufacturing site operations.

• Journal of Korea Society of Industrial Information Systems
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• v.24 no.2
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• pp.65-80
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• 2019

With the advent of the ICT-based 4th industrial revolution, the convergence of the manufacturing industry and ICT seems to be the new breakthrough for achieving the company's competitiveness and play a role on the key element for accelerating the revival of the manufacturing industry. When the smart factory is implemented, each plant can analyze the quantity of data collected, build the data-driven operation systems which can make decisions, and ultimately discover the correlation among many events in the manufacturing sites. As the customers' needs become diversified more and more, it is required for the company to change its operating method from large quantity batch production systems to customizable and flexible manufacturing systems. For performing this requirements, it is essential for the company to adopt the smart factory. Based on technology acceptance model (TAM), this study investigates the factors influencing continuous use intention and switching intention of the smart factory. To do so, a questionnaire survey is conducted both online and offline. 122 samples are used for the study analysis. The results of this study will provide many implications with many researchers and practitioners relevant smart factories.

• Information Systems Review
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• v.19 no.2
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• pp.95-113
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• 2017

The advent of information communication technology or the Fourth Industrial Revolution facilitated the fusion of equipment and management systems, such as Manufacturing Execution System, Enterprise Resource Planning, and Product Lifecycle Management, in the successful implementation of smart factories. The government supports the early adoption of these systems in small and medium companies to enhance their global competitiveness in producing products that can be recognized in a dramatically changing manufacturing environment. This study introduces smart factories to improve company competitiveness and address influences from the government assistance, CEO leadership, external consultancy, and organizational participation. We analyzed 101 results received from the questionnaires circulated to small- and medium-sized manufacturing companies. Given a successful smart factory implementation, company competitiveness is the factor that mostly influences organizational participation, government assistance, external consultancy, and CEO leadership. This study suggests several perspectives to implement a smart factory, which is the most important aspect of company competitiveness.

• 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 Process Engineers
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• v.20 no.5
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• pp.121-127
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• 2021

Reverse offset printing is considering as an emerging technology for printed electronics owing to its environmentally friendliness and cost-effectiveness. In reverse offset printing, selecting the materials for cliché and blanket is critical because of its minimum resolution, registration errors, aspect ratio of reliefs, pattern area, and reusability. Various materials such as silicon, quartz, glass, electroplated nickel plates, and imprinted polymers on rigid substrates can be used for the reverse offset printing of cliché. However, when new structures are designed for specific applications, new clichés need to re-fabricated each time employing multiple time-consuming and costly processes. Therefore, by modifying the blanket materials containing the printing ink, several new structures can be easily created using the same cliché. In this study, we investigated various elastomeric materials and evaluated their applicability for designing a highly stretchable blanket with controlled elastic deformation to implement tunable reverse offset printing.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.44 no.4
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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.