• 연구논문집
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• s.27
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• pp.167-174
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• 1997

Point of Production/Manufacturing Execution Systems are an essential component of operations in today's competitive business environments, which require greater production efficiency and effectiveness. POP/MES focuses on the valuing-adding processes, helping to reduce manufacturing cycle time, improve product quality, reduce WIP, reduce or eliminate paperwork between shifts, reduce lead time and empowering plant operations staff. In this paper, we implement POP/MES to manage real-time plant floor data which is gathered by I/O server into database management system. I/O server is a software allows data exchange between factory real-time database and several hardware devices such as PLC, DCS, robot and sensor through ethernet TCP/IP protocol.

• Journal of Information Technology Applications and Management
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• v.14 no.4
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• pp.31-41
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• 2007

In order to keep global competitiveness, most of small and medium size manufacturing companies require to equip a product information management system which collects and analyzes the data generated at the manufacturing lines and then provides information for manager or worker to make a decision. However, these companies have a cost problem for adopting the enterprise resource planning system mostly used in large companies. To overcome this problem, we suggest an IOCP-based server for the product information management system suitable for small and medium size manufacturing companies. The basic concept of suggested server is that it is possible to process concurrently the connection requests coming from data collector and client by using the facility of asynchronous notification of OS and protect a server against overload by using the thread pool.

• Journal of Institute of Convergence Technology
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• v.3 no.2
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• pp.45-49
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• 2013

Instant custom embroidered souvenir manufacturing system was studied. Recently customers want to get individually specialized souvenir. We present a modular manufacturing system and implementation of image processing, conversion of punching data. The manufacturing system consist of main module, photographing module and U/I module. We can change the system easily through modularization. Image Processing was necessary for making punching data. We developed sketch typed image processing and image processing which used brightness. Brightness type is suitable for instant embroidered souvenir. This study showed that fusion of embroidery technology and image processing technology can make a new business successfully.

• Korean Journal of Computational Design and Engineering
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• v.13 no.2
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• pp.89-97
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• 2008

Today's manufacturing environment is becoming a distributed manufacturing process in which a unique and specialized technological background is required in specific domains rather than having a single company execute all the manufacturing processes. This phenomenon is especially true in the automotive industry, where the sharing of product data between companies is rampant; however, this kind of interoperability causes many problems. When each company has its own method of managing product data, the sharing of product data in a distributed environment is a major problem. A data translator module or a data mapping module had to be developed for the exchange of data in heterogeneous systems of product data management (PDM); moreover, this type of module must be continually changed and improved due to the fact that PDM systems change for many reasons. In addition, the growth in corporate partnerships deepens the burden of developing and maintaining this module and creates further data exchange problems due to the increasing complexity of the system. This paper introduces a way of exchanging product data among heterogeneous PDM systems through the use of OpenPDM, which is a kind of virtual data warehouse. The implementation of a PDM integrating system is also discussed with respect to the requirement for a logical integration of product data which are physically distributed.

• Journal of Convergence for Information Technology
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• v.7 no.5
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• pp.177-182
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• 2017

As major ICT technologies such as IoT, cloud computing, and Big Data are being applied to manufacturing, smart factories are beginning to be built. The key of smart factory implementation is the ability to acquire and analyze data of the factory. Therefore, the need for a big data analysis platform is increasing. The purpose of this study is to construct a platform for big data analysis of manufacturing process and propose integrated method for analysis. The proposed platform is a RHadoop-based structure that integrates analysis tool R and Hadoop to distribute a large amount of datasets. It can store and analyze big data collected in the unit process and factory in the automation system directly in HBase, and it has overcome the limitations of RDB - based analysis. Such a platform should be developed in consideration of the unit process suitability for smart factories, and it is expected to be a guide to building IoT platforms for SMEs that intend to introduce smart factories into the manufacturing process.

• Smart Media Journal
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• v.10 no.3
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• pp.23-30
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• 2021

Although the manufacturing time series data clustering technique is an important grouping solution in the field of detecting and improving manufacturing large data-based equipment and process defects, it has a disadvantage of low accuracy when applying the existing static data target clustering technique to time series data. In this paper, an evolutionary computation-based time series cluster analysis approach is presented to improve the coherence of existing clustering techniques. To this end, first, the image shape resulting from the manufacturing process is converted into one-dimensional time series data using linear scanning, and the optimal sub-clusters for hierarchical cluster analysis and split cluster analysis are derived based on the Pearson distance metric as the target of the transformation data. Finally, by using a genetic algorithm, an optimal cluster combination with minimal similarity is derived for the two cluster analysis results. And the performance superiority of the proposed clustering is verified by comparing the performance with the existing clustering technique for the actual manufacturing process image.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.21 no.1
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• pp.149-162
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• 2021

This paper established a new smart factory based on manufacturing data for an introductory company focusing on the personalized cosmetics manufacturing industry. We build on an example of a system that collects, manages, and analyzes documents and data that were previously managed by CGMP-based analog for data-driven use. To this end, we have established a system that can collect all data in real time at the production site by introducing artificial intelligence smart factory platform LINK5 MOS and POP system, collecting PLC data, and introducing monitoring system and pin board. It also aims to create a new business cluster space based on this project.

• 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.

• IE interfaces
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• v.22 no.1
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• pp.85-94
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• 2009

Nowadays, most of the major manufacturing companies prepare their manufacturing schedule using package based solutions. Even though the accuracy of the detail scheduling result is high at implementation, however, it is low during maintenance period. The main cause of low accuracy during maintenance period is due to difficulties in maintaining the accurate level of master data. In this paper, we propose to easily maintain setup time, which is one of the most important factors required in master data to achieve good scheduling result, after changing job. This paper is mainly focused on how to deduce the factors that influence the setup time in a roll-based manufacturing field with print operations. For this purpose, we employed rule based algorithm and applied for deciding setup time for the existing product items. Likewise, it can be applied to new items without any complex setup procedures, and, finally, it displays the result of the real setup-time and calculated setup-time.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2005.06a
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• pp.460-463
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• 2005

Gear is an important machine element to be used transmission in case short between axis. We drew gear using automatic design program to solve problem when it draw gear. We manufactured gears that it have different pressure angles using W-EDM. And we got a 2D profile of manufactured gear using reverse engineering. So we got to manufacturing error in comparison with CAD data and measured data. In result we could manufacture precise gear through improvement of manufacturing processes.