• Transactions of Materials Processing
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• v.31 no.2
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• pp.103-108
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• 2022

Systematic quality control based on real time data is required for modern factories. This study introduced a method of predicting punch wear in the trimming process of automobile parts. Based on monitoring data of the mass production process using a bolt-type piezo sensor, it was shown that precursor symptoms of die wear could be predicted from the change in load pattern with respect to production volume. The load pattern that changed according to the wear of the die was verified by numerical analysis.

• 중국학논총
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• no.63
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• pp.205-224
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• 2019

The results of the studies of China and Vietnam are as follows. First of all, in China, the labor market in China has been fully completing laws and regulations since the implementation of the labor contract law in 2008. Specifically, we analyzed the labor market in China for labor contracts, recruitment, and minimum wage. Next, in Vietnam, which the tertiary and quaternary industries are rapidly developing. The labor market is expected to increase because demand for foreign manpower, as the advancement of retail, finance, tourism services, Smart factories in the textile and sewing- do. The limitations of this study, however, are that there is not enough data to utilize official data for labor market analysis in China and Vietnam. If a practical investigation is conducted for analyzing the labor market in Vietnam due to the changes in the labor market

• Journal of Venture Innovation
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• v.5 no.2
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• pp.111-127
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• 2022

With the progress of the 4th industrial revolution, interest in smart factories is increasing. The government is implementing a smart factory support project for small and medium-sized manufacturing companies. Therefore, in this study, factors influencing small and medium-sized enterprises(SME's) intention of smart factory acceptance were analyzed. In particular, it focused on how the perception of government support affects intention of smart factory acceptance. For the empirical analysis, a research model was established by reflecting the characteristics of SMEs and the technical factors of the smart factory centering on the technology acceptance theory. Based on the model set in this way, a questionnaire survey was conducted for employees of SMEs. In this study, a total of 231 samples of valid data were used for analysis. The empirical analysis results are as follows. It was analyzed that performance expectancy, social influence, technology utilization capability, CEO will, and employee resistance to innovation, all introduced as research variables, had a significant effect on the use intention of smart factory acceptance. In particular, it was found that employees' resistance to innovation had a negative (-) effect on their use intention. Meanwhile, to analyze the moderating effect of government support, it was divided into a group with high expectations for government support and a group with low expectations. As a result, it was found that there was a difference in the effect of CEO's will, employees' resistance to innovation, and social influence on the use intention. On the other hand, no significant difference was found in the relationship between performance expectancy, technology utilization capability on the use intention. Based on the empirical analysis results, the academic and practical implications of this study were presented.

• Journal of Information Technology Services
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• v.21 no.2
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• pp.97-107
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• 2022

Recently, interest in smart factories is increasing. Investments to improve intelligence/automation are also being made continuously in manufacturing plants. Facility automation based on sensor data collection is now essential. In addition, we are operating our factories based on data generated in all areas of production, including production management, facility operation, and quality management, and an integrated standard information system. When producing LCD polarizer products, it is most important to link trace information between data generated by individual production processes. All systems involved in production must ensure that there is no data loss and data integrity is ensured. The large-capacity data collected from individual systems is composed of key values linked to each other. A real-time quality analysis processing system based on connected integrated system data is required. In this study, large-capacity data collection, storage, integration and loss prevention methods were presented for optimization of LCD polarizer production. The identification Risk model of inspection products can be added, and the applicable product model is designed to be continuously expanded. A quality inspection and analysis system that maximizes the yield rate was designed by using the final inspection image of the product using big data technology. In the case of products that are predefined as analysable products, it is designed to be verified with the big data knn analysis model, and individual analysis results are continuously applied to the actual production site to operate in a virtuous cycle structure. Production Optimization was performed by applying it to the currently produced LCD polarizer production line.

• KIPS Transactions on Computer and Communication Systems
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• v.2 no.7
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• pp.277-282
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• 2013

Recently, there is a growing need for an open software platform where developers easily write intelligent motion control applications for smart cars, smart robots, smart factories, and so on. To this end, a general-purpose operating system with rich functionalities and various hardware supports can be a candidate for such a platform, but it is known to have limitations in guaranteeing the responsiveness of individual applications. In this paper, to assess the suitability of Linux to be such a platform, we evaluate the real-time performance of Xenomai-patched Linux on an ARM-based processor Exynos4210 with motion control applications. Experimental results show that it is possible to stably provide motion cycle times below 1ms to such applications even with background workloads.

• Journal of Convergence for Information Technology
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• v.8 no.3
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• pp.169-174
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• 2018

Today, the world has reached 'Industry 4.0'. Industry 4.0 has high uncertainty in various aspects because it is based on building a smart chain where the various elements that make up the industry can communicate with each other. Based on the above facts, based on the researches of the previous researchers, we have searched for the countermeasures of small and medium sized manufacturing companies in Korea in order to minimize the negative aspects of establishing the basic concepts and functioning of Industry 4.0. As a result, efforts to accurately identify and address the uncertainties of Industry 4.0 in a variety of ways will help to drive business growth and economic growth in the country through smart factories, which are at the heart of Industry 4.0.

• Journal of Practical Engineering Education
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• v.9 no.1
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• pp.49-56
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• 2017

Recently, due to Industry 4.0, there is a great interest in smart factory for productivity improvement and customer satisfaction in manufacturing industry, and construction is also actively pursued by government support. In particular, data integration and fieldbus communication technology to build an efficient production system are essential. Fieldbus is an open control system that is not tied to a specific vendor system and has various advantages such as compatibility with other products, accuracy of data transmission, and remote diagnosis. However, there are no educational equipment for training field buses, training courses and examples for practical training, and there are many limitations in improving the practical skills needed for building smart factories in the industrial field. Therefore, this study develops PLC based fieldbus education equipment and training course based on previous research results that selected PLC and communication technology suitable for domestic industry field for practical fieldbus training and develops the training program of Ethernet IP, Profibus DP, Modbus, CC-Link, and DeviceNet. In addition, it is confirmed that the control and remote diagnosis of distributed field devices are possible by data collection and monitoring.

• 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.

• Journal of the Institute of Convergence Signal Processing
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• v.23 no.4
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• pp.216-221
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• 2022

The 4th industrial revolution is accelerating the transition to digital innovation in various aspects of our daily lives, and efforts for manufacturing innovation are continuing in the manufacturing industry, such as smart factories. The 4th industrial revolution technology in manufacturing can be used based on AI, big data, IoT, cloud, and robots. Through this, it is required to develop a technology to establish a production facility data collection and analysis system that has evolved from the existing automation and to find the cause of defects and minimize the defect rate. In this paper, we implemented a system that collects power, environment, and status data from production facility sites through IoT devices, quantifies them in real-time in a cloud computing environment, and displays them in the form of MQTT-based real-time infographics using widgets. The real-time sensor data transmitted from the IoT device is stored to the cloud server through a Rest API method. In addition, the administrator could remotely monitor the data on the dashboard and analyze it hourly and daily.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.42 no.2
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• pp.151-162
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• 2022

Prefab members have attracted attention because they can be mass-produced in factories through smart construction technology. For prefab prestressed concrete girders, it is important to manage the shapes of the girders properly from production to the pre-installation stage for consistency with the prefab floor plate during the erection process. This paper presents a camber reconstruction method using collocated strain measurements from the top and bottom of the prefab girder. In particular, the camber reconstruction method is applied to measured strain data in which the time-dependent behavior of concrete is considered after the introduction of prestress. Through Monte Carlo numerical simulations, the statistical accuracy of the reconstructed camber for a limited number of sensors, measurement errors, and nonlinear time-dependent behaviors are analyzed and validated.