• Industry Promotion Research
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• v.2 no.1
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• pp.1-7
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• 2017

In the case of manufacturing companies, various types of data are collected. Many of these data can be used as useful information for product reliability evaluation. In this study, we first look at data that can be collected by a manufacturing company and related to products, technology, finance, and customers. Next, we will look at the company's business management system, scientific journals, test and marketing survey data, etc., as sources of data. Next, look at what kind of data is collected over the product life cycle to evaluate the reliability of the product. In the development stage of the product, reliability test is performed for each component, and reliability data is collected by performing reliability test at the subsystem and system level. On the other hand, at the manufacturing stage, data on the functional test and the design change test of the product are collected, and at the field stage, the problem of the product is detected in the field and collected in the form of data. Finally, let's look at what you need to do to make a reasonable analysis later in your data collection.

• The Journal of Society for e-Business Studies
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• v.27 no.2
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• pp.205-218
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• 2022

Recently, digital transformation in manufacturing has been accelerating. It results in that the data collection technologies from the shop-floor is becoming important. These approaches focus primarily on obtaining specific manufacturing data using various sensors and communication technologies. In order to expand the channel of field data collection, this study proposes a method to automatically collect manufacturing data based on vision-based artificial intelligence. This is to analyze real-time image information with the object detection and tracking technologies and to obtain manufacturing data. The research team collects object motion information for each frame by applying YOLO (You Only Look Once) and DeepSORT as object detection and tracking algorithms. Thereafter, the motion information is converted into two pieces of manufacturing data (production performance and time) through post-processing. A dynamically moving factory model is created to obtain training data for deep learning. In addition, operating scenarios are proposed to reproduce the shop-floor situation in the real world. The operating scenario assumes a flow-shop consisting of six facilities. As a result of collecting manufacturing data according to the operating scenarios, the accuracy was 96.3%.

• 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.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.18 no.3
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• pp.159-166
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• 2018

This paper introduces a manufacturing facility visualization system for the monitoring of smart factories. With the development of technology and the emergence of such terms as the Fourth Industrial Revolution and Industry 4.0, technologies for smart factories are becoming more important. A Manufacturing Execution System that can improve productivity and help decision making by monitoring production plants in real-time is one of the key technologies for smart factories. The application of digital twin technology for more accurate monitoring technology is also an important issue. However, digital twin implementations require an integrated infrastructure that can integrate facility data from multiple manufacturers. Therefore, this paper designs and develops a visualization program that can verify real-time information of facilities using data collection system based on international standard protocol for heterogeneous collection and monitoring of facility data. This allows a factory to consolidate equipment data from multiple manufacturers and to view them in real-time.

• Proceedings of the Korea Information Processing Society Conference
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• 2019.10a
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• pp.350-353
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• 2019

4차 산업혁명 시대에 접어들면서 제조업은 공장 내 설비와 기계에 Arduino 센서를 설치하여 데이터를 실시간으로 수집, 분석하는 스마트 팩토리로 전환되었다. 이에 Digital Twin이라는 개념이 생겨난다. Digital Twin이란 컴퓨터에 현실 속 사물의 쌍둥이를 만들고, 현실에서 발생할 수 있는 상황을 컴퓨터로 시뮬레이션 함으로써 결과를 예측하는 기술을 말한다. 본 논문에서는 제조환경에서 발생할 수 있는 물리적인 동작 환경을 시뮬레이션하고, PLC 데이터와 수집된 센서 데이터를 이용하여 실시간으로 공정과정을 모니터링할 수 있는 Virtual Digital Twin System을 제안한다. 본 시스템은 PLC Hardware와 Arduino 센서, 사용자가 접근할 수 있는 PC 및 Web Page로 이루어진다. 제조환경에서의 PLC Hardware를 3D 모델링하여 제공한다. 또한 기기에 부착되어있는 Arduino 센서에서 발생하는 데이터를 수집하고, 분석하여 후에 발생할 수 있는 결함에 대하여 대처할 수 있도록 한다.

• The Journal of Bigdata
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• v.3 no.1
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• pp.95-103
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• 2018

In recent years, many people in the manufacturing field have been making efforts to increase efficiency while analyzing manufacturing data generated in the process according to the development of ICT technology. In this study, we propose a data mining based manufacturing process using decision tree algorithm (CHAID) as part of a smart factory. We used 432 sensor data from actual manufacturing plant collected for about 5 months to find out the variables that show a significant difference between the stable process period with low defect rate and the unstable process period with high defect rate. We set the range of the stable value of the variable to determine whether the selected final variable actually has an effect on the defect rate improvement. In addition, we measured the effect of the defect rate improvement by adjusting the process set-point so that the sensor did not deviate from the stable value range in the 14 day process. Through this, we expect to be able to provide empirical guidelines to improve the defect rate by utilizing and analyzing the process sensor data generated in the manufacturing industry.

• 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 Korea Institute of Information, Electronics, and Communication Technology
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• v.14 no.4
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• pp.292-299
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• 2021

A form of data mining software, based on manufacturing-based IIoT environment with the development of IT technologies are increasingly growing. However, it is difficult to evaluate the software quality in the same form as general software due to the characteristics of the software of a manufacturing company that has a large amount of data that needs to be carried out with big data and data mining. In addition, in a manufacturing-based environment where heterogeneous equipment and software are mixed, it is difficult to perform quality judgment on software used by applying existing quality characteristics. Therefore, in this paper, the characteristics of the manufacturing base are investigated, and a software quality evaluation model suitable for it is developed and evaluated.

• Journal of the KSME
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• v.32 no.4
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• pp.371-378
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• 1992

발전용 터빈 부품중 회전체인 로터의 수명평가를 간단히 소개하고 수명평가시 고려되는 주요 재질특성에 대하여는 간단한 설명과 데이터를 이용하는 방법을 기술했다. 정확한 수명평가를 위해서는 금속학적인 고찰을 병행하여 경년열화와 효과를 고려하고, 실제 가동 조건과 동일시할 수 있는 각종 데이터를 적용해야 하나 본문에서와 같이 실험에 따르는 제약이 많아 추정 또는 가공된 데이터를 이용한다. 신뢰성 높은 수명평가를 위해선 각종 데이터 확보를 위한 실험장치의 개선과 실험방법의 개발이 매우 중요하다고 본다. 또한 현존하는 재료제조 기술상 초기부터 일 정한 허용결함을 인정하면서 설계, 제조, 가동되고 있는 각종 설비에 경제성을 극대화시키면서 동시에 안전을 확보하는 것은 매우 어렵지만 피할 수 없는 당면과제라 생각한다.

• Smart Media Journal
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• v.8 no.3
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• pp.95-103
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• 2019

The global manufacturing industry has reached the limit to growth due to a long-term recession, the rise of labor cost and raw material. As a solution to these difficulties, we promote the 4th Industry Revolution based on ICT and sensor technology. Following this trend, this paper proposes the design of a model using manufacturing data in the protection film process for smart manufacturing innovation. In the protective film process, the manufacturing data of temperature, pressure, humidity, and motion and thermal image are acquired by various sensors for the raw material blending, stirring, extrusion, and inspection processes. While the acquired manufacturing data is stored in mass storage, A.I. platform provides time-series image analysis and its visualization.