• International Journal of Internet, Broadcasting and Communication
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• v.13 no.2
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• pp.7-13
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• 2021

Machine vision is a technology that helps the computer as if a person recognizes and determines things. In recent years, as advanced technologies such as optical systems, artificial intelligence and big data advanced in conventional machine vision system became more accurate quality inspection and it increases the manufacturing efficiency. In machine vision systems using deep learning, the image quality of the input image is very important. However, most images obtained in the industrial field for quality inspection typically contain noise. This noise is a major factor in the performance of the machine vision system. Therefore, in order to improve the performance of the machine vision system, it is necessary to eliminate the noise of the image. There are lots of research being done to remove noise from the image. In this paper, we propose an autoencoder based machine vision system to eliminate noise in the image. Through experiment proposed model showed better performance compared to the basic autoencoder model in denoising and image reconstruction capability for MNIST and fashion MNIST data sets.

• Journal of Powder Materials
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• v.27 no.2
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• pp.111-118
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• 2020

The convergence of artificial intelligence with smart factories or smart mechanical systems has been actively studied to maximize the efficiency and safety. Despite the high improvement of artificial neural networks, their application in the manufacturing industry has been difficult due to limitations in obtaining meaningful data from factories or mechanical systems. Accordingly, there have been active studies on manufacturing components with sensor integration allowing them to generate important data from themselves. Additive manufacturing enables the fabrication of a net shaped product with various materials including plastic, metal, or ceramic parts. With the principle of layer-by-layer adhesion of material, there has been active research to utilize this multi-step manufacturing process, such as changing the material at a certain step of adhesion or adding sensor components in the middle of the additive manufacturing process. Particularly for smart parts manufacturing, researchers have attempted to embed sensors or integrated circuit boards within a three-dimensional component during the additive manufacturing process. While most of the sensor embedding additive manufacturing was based on polymer material, there have also been studies on sensor integration within metal or ceramic materials. This study reviews the additive manufacturing technology for sensor integration into plastic, ceramic, and metal materials.

• Journal of Practical Engineering Education
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• v.11 no.2
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• pp.283-289
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• 2019

Recently, product mobility, data compatibility and communication connectivity have become very important to the control system, depending on the application of smart manufacturing. Accordingly, embedded systems are essential in all industries including home appliances, telecommunication, and national defense. Therefore, the demand for embedded system development personnel is increasing further, and education and training programs are needed to combine the practical skills of industrial sites, including programming skills and hardware. Currently, embedded system education offers a variety of education centered on Aduino, but this is mostly for beginners and is not sufficient for majors. In addition, while various prototype studies related to embedded systems are active, the training and training programs for working-level human resources needed at industrial sites are very scarce. Therefore, in order to foster the working personnel of the embedded system for the application of smart manufacturing, this paper selected the capability unit through in-depth interviews and survey analysis of 10 experts based on NCS, and developed education and training programs and contents.

• Journal of Korean Institute of Industrial Engineers
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• v.40 no.6
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• pp.555-567
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• 2014

Over 40 years, the domestic manufacturing industry of Korea played the vital role in creating national wealth, employment and innovation in its economy. In 2011, industrial products made up 99.7 percent of exports, and formed 31.2 percent of GDP. Given those facts, the research of manufacturing systems engineering for higher manufacturing efficiency and its industrial applications have been essential for Korea's economic growth. In this paper, for celebrating the 40th anniversary of the foundation of Korea institute of industrial engineers (KIIE), the research results and its industrial applications of KIIE are reviewed and summarized in the area of manufacturing systems engineering over past 40 years. Directions of future manufacturing system and the role of industrial engineers for our continuous economic growth are also mentioned.

• IEMEK Journal of Embedded Systems and Applications
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• v.13 no.1
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• pp.25-32
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• 2018

In this paper, we present a proposed monitoring system for smart factories with several aspects, including information gathering, analysis, control, and display that relate to concurrently operation processes in the factory area. This paper proposes a monitoring and management system for industrial automation. In particular, it uses an Internet of Thing (IoT) device with a data protocol unit to convert the industrial protocols and transfer the information on various parameters. In the case of data communication, the proposed monitoring system is designed to support users to remotely manage with the cloud server by implementing conversion between Modbus RTU and Modbus TCP of protocol communications. The proposed communication technique has been verified by experiments.

• Journal of Advanced Navigation Technology
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• v.27 no.1
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• pp.111-118
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• 2023

For the implementation of a smart factory, it is necessary to collect data by connecting various sensors and devices in the manufacturing environment and to diagnose or predict failures in production facilities through data analysis. In this paper, to predict the residual useful lifetime of milling insert used for machining products in CNC machine, weight k-NN algorithm, Decision Tree, SVR, XGBoost, Random forest, 1D-CNN, and frequency spectrum based on vibration signal are investigated. As the results of the paper, the frequency spectrum does not provide a reliable criterion for an accurate prediction of the residual useful lifetime of an insert. And the weighted k-nearest neighbor algorithm performed best with an MAE of 0.0013, MSE of 0.004, and RMSE of 0.0192. This is an error of 0.001 seconds of the remaining useful lifetime of the insert predicted by the weighted-nearest neighbor algorithm, and it is considered to be a level that can be applied to actual industrial sites.

• Journal of Korea Society of Industrial Information Systems
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• v.22 no.5
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• pp.31-37
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• 2017

In this Paper, we have Improved the Manufacturing Process of Liquid Aluminum Sulfate using the Design of Automated Measurement Systems. The Manufacturing Process of Liquid Aluminum Sulfate uses a Large Weight. The Quality of a Product Depends Highly on the Proportion of the Raw Material Input in the Production Process. Therefore, it is Very Important to Accurately Measure the Amount of Raw Material. For Automation Design, Load Cell Sensor which can Measure Large Weight Accurately and PLC Technology which is most used in Automation Process are Applied. The Content of Aluminum Oxide in the Aluminum Sulfate Produced before the Automation Design Varies from 8.023% to 8.250%. However, after Automation Design, the Amount of Change from 8.09% to 8.19% was Greatly Reduced. As a Result, we could Reduce the Quality Defect rate Due to Weighing Errors and Reduce Safety Accidents by Applying Automation System.

• Journal of the Korea Society of Computer and Information
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• v.28 no.10
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• pp.171-178
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• 2023

In this paper, we propose a smart automated warehouse to maximize space utilization. Previous elevator-type automatic warehouses were designed with a maximum payload of 100kg on trays, which has the problem of extremely limiting the number of pallets that can be loaded within the space. In this paper, we design a smart warehouse that can maximize space utilization with a maximum vertical stiffness of 300kg. As a result of the performance evaluation of the implemented warehouse, the maximum payload was 500.6kg, which satisfied the original design and requirements, the lifting speed was 0.5m/s, the operating noise of the device was 67.1dB, the receiving and forwarding time of the pallet was 36.92sec, the deflection amount was 4mm, and excellent performance was confirmed in all evaluation items. In addition, the PLC control method, which designs the control UI and control panel separately, was integrated into the PC system to improve interoperability and maintainability with various process management systems. In the future, we plan to develop it into a fully automatic smart warehouse by linking IoT sensor-based logistics robots.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.18 no.2
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• pp.197-207
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• 2018

SMEs manufacturing Factory, which are small-scale production systems of various types, mass-produce and sell products in order to meet customer needs. This means that the company has an excessive amount of material supply to reduce the loss due to lack of inventory and high inventory maintenance cost. And the products that fail to respond to the demand are piled up in the management warehouse, which is the reality that the storage cost is incurred. To overcome this problem, this paper uses ARIMA model, a time series analysis technique, to predict demand in terms of seasonal factors. In this way, demand forecasting model based on economic order quantity model was developed to prevent stock shortage risk. Simulation is carried out to evaluate the effectiveness of the development model and to demonstrate the effectiveness of the development model as applied to SMEs in the future.

• KIPS Transactions on Computer and Communication Systems
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• v.7 no.5
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• pp.111-118
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• 2018

The effects of the Fourth Industrial Revolution on manufacturing are spreading by policies to secure or strengthen the manufacturing competitiveness of each country. Strengthening policies on manufacturing necessitate nurturing manpower for smart manufacturing. This study examines the difference of the experts' perception about the educational curriculum to develop the knowledge of Product Lifecycle which covers the whole knowledge area of product development among the knowledge areas aimed at fostering the manpower of smart manufacturing for the $4^{th}$ Industrial Revolution Era. Experts were aware that future developments in digital development, production, and new product development are most important, and that they feel that the whole knowledge area is generally weak. In this study, the implications for the development of educational curriculum in the future are derived from the perception difference of knowledge on Product Lifecycle obtained through expert survey.