• Journal of the Society of Disaster Information
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• v.18 no.2
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• pp.351-363
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• 2022

Purpose: The objective of this research is to find manufacturing employment clusters of the disaster safety sector in South Korea. Method: The LISA(Local Indicator of Spatial Association) analysis method is applied to the employment data of 229 local governments categorized by the 2019 Korean Standard Industry Classification and Disaster Safety Industry Special Classification. The LISA method identifies the spatial dependency of employment and the spatial cluster of industries. Result: Three research findings are summarized. First, employment of the disaster safety industry in South Korea occupies about six percent of the total manufacturing industry. The annual proportion is in increasing trend. Second, the employment cluster of the disaster safety industry is located in the western side of the Seoul metropolitan region. Third, manufacturing businesses of industrial safety goods preventing industrial accidents are concentrated in regions of Busan, Ulsan, Changwon, Gyeongnam, and Gimhae, where heavy and chemical industries and industrial complexes are formed. Conclusion: Investment and promotion policies are suggested to the manufacturing employment clusters of the disaster safety industry for fostering these regions. Research results can be used to the better policies for industrial development and employment improvement of manufacturing clusters of the disaster safety industry in South Korea.

• International Journal of Quality Innovation
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• v.6 no.2
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• pp.59-89
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• 2005

Manufacturing Resources Planning (MRPII) is one kind of manufacturing information system that can help manufacturing companies gain competitive advantages. It is estimated that more than one hundred MRPII systems are available in the market, many of them are mature enough to solve most operational issues in accordance with users' requirements. More often than not, many of these systems provide more functions than a company expects. Manufacturing companies worldwide have attempted to implement these MRPII systems, however, many companies experienced failure (Turbide, 1996) due to managerial rather than technical issues. The authors propose an approach utilising a roadmap to integrate BPR and the MRPII implementation in order to overcome this difficulty. A detail road map is developed to guide this implementation, which is designed using a hierarchical analysis technique known as Integrated DEFinition Method (IDEF). IDEF is a systematic manufacturing management and integration-modeling tool. The proposed approach is implemented and illustrated using a reference company and the results indicated that 66% reduction in errors for maintaining the bills of materials system; 99% reduction in time to carry out material requirement planning; and 70% reduction in time previously taken for non-productive discussions.

• Journal of the Korean Society of Manufacturing Process Engineers
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• v.18 no.8
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• pp.1-7
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• 2019

Materials that can be used for 3D printing have been developed in terms of phase and functionality. Materials should also be easily printed with high accuracy. In recent years, the concept of 4D printing has been extended to materials whose physical properties such as shape or volume can change depending on the environment. Typically, such high-performance 3D printing materials include bio-inks and inks for sensors. This study deals with the optimization of the manufacturing method to improve the functional properties of the pressure sensitive material, which can be used as a sensor based on change of the resistance according to the pressure. Specifically, the number of milling for dispersion, the ratio of hardener for controlling elasticity, and the content of MWCNTs were optimized. As a result, a method of manufacturing a highly sensitive pressure-sensitive ink capable of use in 3D printing was introduced.

• Journal of Platform Technology
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• v.9 no.3
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• pp.52-62
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• 2021

Quality control is critical at manufacturing sites and is key to predicting the risk of quality defect before manufacturing. However, the reliability of manual quality control methods is affected by human and physical limitations because manufacturing processes vary across industries. These limitations become particularly obvious in domain areas with numerous manufacturing processes, such as the manufacture of major nuclear equipment. This study proposed a novel method for predicting the risk of quality defects by using natural language processing and machine learning. In this study, production data collected over 6 years at a factory that manufactures main equipment that is installed in nuclear power plants were used. In the preprocessing stage of text data, a mapping method was applied to the word dictionary so that domain knowledge could be appropriately reflected, and a hybrid algorithm, which combined n-gram, Term Frequency-Inverse Document Frequency, and Singular Value Decomposition, was constructed for sentence vectorization. Next, in the experiment to classify the risky processes resulting in poor quality, k-fold cross-validation was applied to categorize cases from Unigram to cumulative Trigram. Furthermore, for achieving objective experimental results, Naive Bayes and Support Vector Machine were used as classification algorithms and the maximum accuracy and F1-score of 0.7685 and 0.8641, respectively, were achieved. Thus, the proposed method is effective. The performance of the proposed method were compared and with votes of field engineers, and the results revealed that the proposed method outperformed field engineers. Thus, the method can be implemented for quality control at manufacturing sites.

• Journal of Ocean Engineering and Technology
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• v.24 no.5
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• pp.75-80
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• 2010

Fiberglass-Reinforced Plastic (FRP) composites have been used for small fishing boats and leisure boats for many years. These composites have different physical characteristics, depending on the constitution of lay-up and manufacturing method. Recently, new manufacturing methods, such as vacuum infusion, have been used to make the composites lighter and stronger. In this research, the mechanical properties of fiberglass reinforcements with constitution of lay-up, manufacturing method, and two different resins were investigated experimentally. It was found that the mechanical properties of FRP composites increased with increasing thickness, with the use of vacuum infusion method, and with the use of vinyl ester resin. The mechanical properties of diverse FRP composites can be used as a practical guide for selecting appropriate materials for specific applications.

• Journal of Korean Society of Occupational and Environmental Hygiene
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• v.19 no.3
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• pp.240-249
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• 2009

HRSG(Heat Recovery Steam Generator) building is large enclosed structure included various heat sources. This building needs to appropriately keep internal air temperature for worker's safety and operability of control devices. In this study, ventilation analysis is performed to find proper ventilation method for temperature control. Ventilation analysis is applied to entire internal space of the building with standard $k-{\varepsilon}$ model and enhanced wall treatment because of large size of the structure. And the ventilation method is considered natural and forced convection with two louver structures which has damper or not. Louver structure affect directly air circulation in near HRSG and lower region of the building. Forced ventilation provides strong inertial force which cause upward airflow. From the analysis, it is found that design requirement for internal air temperature can be satisfied by forced ventilation method with louver structure without damper.

• Journal of Korean Institute of Industrial Engineers
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• v.36 no.3
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• pp.154-163
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• 2010

Process monitoring of output variables affecting final performance have been mainly executed in semiconductor manufacturing process. However, even earlier detection of causes of output variation cannot completely prevent yield loss because a number of wafers after detecting them must be re-processed or cast away. Semiconductor manufacturers have put more attention toward monitoring process inputs to prevent yield loss by early detecting change-point of the process. In the paper, we propose the method to efficiently monitor functional input variables in multi-phase semiconductor manufacturing process. Measured input variables in the multi-phase process tend to be of functional structured form. After data pre-processing for these functional input data, change-point analysis is practiced to the pre-processed data set. If process variation occurs, key variables affecting process variation are selected using contribution plot for monitoring efficiency. To evaluate the propriety of proposed monitoring method, we used real data set in semiconductor manufacturing process. The experiment shows that the proposed method has better performance than previous output monitoring method in terms of fault detection and process monitoring.

• Korean Journal of Computational Design and Engineering
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• v.14 no.4
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• pp.242-253
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• 2009

Nowadays, manufacturing industry has been making its effort not only for productivity elevation but also for cost reduction in order to survive in the global market which is more and more challenging. In this paper, the method for planning of digital manufacturing system is proposed and door trim assembly system is determined as the subject of our research. First of all, the process sequence is generated based on the product analysis. And, the static and dynamic relationships between system components are represented using IDEF0 and UML model. The working time is estimated through the regression analysis based on MODAPTS method. According to the system configuration strategy, initial concept system layout is implemented 3D virtual environment. The problems caused by bad working motions are detected and modified through the ergonomic analysis using RULA method. According to proposed procedure, digital door trim assembly system is implemented in DLEMIA.

• Journal of the Korean Institute of Intelligent Systems
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• v.21 no.3
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• pp.378-382
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• 2011

This paper develops the core competencies of manufacturing workers, analyze the consciousness structure on the core competencies. As the analyzing method of consciousness structure, ISM(Interpretive Structural Modeling) and FSM(Fuzzy Structural Modeling) are used to classify layers and determine the connection state. However, the element of each layer is frequently changed by data. This paper suggests the method with the point of view that the structure is determined basically and the connection state of the structure model is changeable depending on the method; first to determine structure model by ISM, second to determine connection by FSM. By using this method, the objective structure model, analyzing the consciousness on the core competencies of manufacturing workers, is suggested with specialist confirm.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2021.10a
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• pp.82-84
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• 2021

Through the smart factory construction project, sensor data such as power, vibration, pressure, and temperature are collected from production facilities, and services such as predictive maintenance, defect prediction, and abnormality detection are developed through data analysis. In general, in the case of manufacturing data, because the imbalance between normal and abnormal data is extreme, an anomaly detection service is preferred. In this paper, FFT method is used to extract feature data of manufacturing data as a pre-stage of the anomaly detection service development. Using this method, we classified the produced products and confirmed results. In other words, after FFT of the representative pattern for each product, we verified whether product classification was possible or not, by calculating correlation coefficient.