• Asia-pacific Journal of Multimedia Services Convergent with Art, Humanities, and Sociology
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• v.7 no.9
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• pp.259-269
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• 2017

As emerging the '4th Industrial Revolution' by increasing the necessity of the intelligent system recently, 'Autonomic Control System' also has been the important issue. It is necessary to develop the system collecting data of machines and sensors for the autonomic control system to monitor the target system. But it is difficult to collect data because data formats of machines and sensors of the existing factories differ between each manufacturer. Therefore, this paper presents and implements data collecting and processing system that comprise 3 steps including 'ParseBuffer', 'ProcessData' and 'AddToBuffer' by using 'MTConnect' that is standard manufacturing facility data collecting middleware. Through the suggested system, we can get data in a common format usable in an autonomous control system. As a case study, we experimented with the generation and collection of AGV (Automated Guided Vehicle) data, which is an unattended transportation system in the factory. To accomplish this, we defined the data type in accordance with the MTConnect standard and confirmed the data collected through the proposed system.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.18 no.4
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• pp.219-226
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• 2018

IIoT(Industrial Internet of Thing) means that all manufacturing processes are informed beyond the conventional automation of process automation. The objective of the system is to build an information system based on the data collected from the sensors installed in each process and to maintain optimal productivity by managing and automating each process in real time. Data collected from sensors in each process is unstructured and many studies have been conducted to collect and process such unstructured data effectively. In this paper, we propose a system using Node-RED as middleware for effective big data collection and processing.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2022.05a
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• pp.513-515
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• 2022

Manufacturing data collected using IoT devices in a smart factory environment is generally reliable except for noises caused by external factors. However, unlike manufacturing data that is collected mechanically, process data manually recorded by field-workers can cause problems such as the misspelled entries or the missing entries. Therefore, process data must be validated before being used as training data for artificial intelligence models. In this paper, based on the fact that which is a linear relationship between the power consumption of the MCT machine and the production of the product recorded by the field-workers, we detect anomalous records of the workers using robust linear regression.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.35 no.1
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• pp.39-45
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• 2011

The processes for manufacturing a product differ depending on the characteristics of the product, and the information used or generated by the processes also varies. To implement a flexible and configurable Manufacturing Execution System (MES), a Data Acquisition System (DAS) that takes into consideration the characteristics of the manufacturing system is required. In this study, we design an information acquisition system that can process the information on equipments of a shop floor in real-time and that is adaptive to the changes in the shop floor. The system has a data parser module for flexible processing of the equipment status, a data mapper module to link the equipment status with a manufacturing process, and an SOA-based data integration module to transmit the processed information to other information systems such as MES and ERP. From the results of pilot study, its maintenance is easy even if new equipment or new manufacturing processes are adopted or if the equipments are rearranged.

• Proceedings of the Korea Technology Innovation Society Conference
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• 2017.11a
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• pp.483-497
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• 2017

a본 논문에서는 과학기술정책연구원에서 조사한 2016년 한국기술혁신조사(KIS 2016) 데이터를 이용하여 한국 제조기업의 혁신성과에 영향을 미치는 혁신저해요인에 관한 분석을 진행하였다. KIS 2016 데이터의 제조업 기업 수는 4000개였는데 이 중 혁신 설문에서 제시한 혁신 저해요인 중 아무것도 겪지 않았다고 답한 기업과 응답이 누락된 기업을 제외한 3159개 기업의 데이터를 활용하여 분석을 진행하였으며, 분석 방법으로는 로지스틱 회귀모형을 사용하였다. 자료를 바탕으로 요인분석을 실시하였으며, 그에 따라 혁신 저해요인으로 자금 문제, 기업 역량 요인, 필요 요인 총 세 가지 요인이 추출되었다. 이를 바탕으로 로지스틱 회귀분석 결과 자금 문제와 기업 역량 요인의 경우 혁신 제품의 시장 출시에 정(+)의 영향을 미치는 것으로 나타났으며 필요 요인의 경우 음(-)의 영향을 미치는 것으로 나타나 기업의 혁신 성공에 있어서는 기술혁신의 필요성에 대한 인식 제고가 시급한 것으로 분석되었다.

• Proceedings of the Korea Contents Association Conference
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• 2016.05a
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• pp.341-342
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• 2016

제조 산업에서 키워드로 많이 다뤄지는 4M데이터(man, method, machine, martial)는 생산에 효율성을 높이기 위한 중요한 요소이다. 규모가 작은 기업일수록 4M관련 데이터 관리가 잘 안되고 있다. 관리를 잘하는 기업이라도 수집하고 저장만 하고 있는 현실이다. 본 논문에서는 수집하고 저장되어 있는 4M데이터를 활용해 생산한 상품에 대한 생산당시 상황을 추적할 수 있는 시스템을 제안한다. 제안한 시스템을 이용해 생산라인의 상황을 한 눈에 파악이 가능하고 주로 문제가 발생하는 공정과 관련 요인 파악을 통해 불량률을 줄이는 연구를 할 수 있다.

• KIPS Transactions on Software and Data Engineering
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• v.13 no.1
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• pp.1-16
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• 2024

Over recent years, 6 Sigma has become a key methodology in manufacturing for quality improvement and cost reduction. However, challenges have arisen due to the difficulty in analyzing large-scale data generated by smart factories and its traditional, formal application. To address these limitations, a big data-based 6 Sigma approach has been developed, integrating the strengths of 6 Sigma and big data analysis, including statistical verification, mathematical optimization, interpretability, and machine learning. Despite its potential, the practical impact of this big data-based 6 Sigma on manufacturing processes and management performance has not been adequately verified, leading to its limited reliability and underutilization in practice. This study investigates the efficiency impact of DX SS, a big data-based 6 Sigma, on manufacturing processes, and identifies key success policies for its effective introduction and implementation in enterprises. The study highlights the importance of involving all executives and employees and researching key success policies, as demonstrated by cases where methodology implementation failed due to incorrect policies. This research aims to assist manufacturing companies in achieving successful outcomes by actively adopting and utilizing the methodologies presented.

• Proceedings of the Korea Multimedia Society Conference
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• 2002.11b
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• pp.363-367
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• 2002

일반 중소기업들의 업무는 수작업으로 데이터를 유지하고 있는 수준에 머물고 있는 실정이다. 인건비를 포함한 제반비용의 절감과 업무능률의 향상이 필요하다. 데이터 관리 및 유지를 위해서 효율적이고 신속 정확한 작업처리를 위해 데이터 베이스를 이용한 통합 자원관리 시스템이 구축이 필요하다. 이 프로젝트는 업무 효율을 높이기 위하여 데이터 베이스를 이용한 통합 관리 시스템을 개발하고 용이한 자료 추출을 위한 자료추출 엔진 및 출력 폼 개발 및 Network구성을 통한 분산환경에서의 사무자동화 구현을 목표로 개발하였다.

• The KIPS Transactions:PartA
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• v.16A no.6
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• pp.427-436
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• 2009

Digital virtual manufacturing is a technology that aims for the rapid development of products and the verification of production-process in ways that are more efficient by integrating digital models within the entire manufacturing process. These digital models utilize various information technologies, such as 3D CAD and simulations. Mixed reality, which represents graphical objects for only needed parts against real scene, can bring a more enriched sense of reality to an existing virtual manufacturing system that is in a pure virtual environment, and it can reduce the time and money needed for modeling the environment. This paper suggests a method for planning virtual factory layouts based on mixed reality using legacy datathat are already constructed in the real field. To do this, we developed the method to acquire simulation data from legacy data and process this acquired data for visualization based on mixed reality. And then we construct display system based on mixed reality, which can simulate virtual factory layout with processed data. Developed system can reduce errors related with factory layout by verifying the location and application of equipments in advance before arrangement of real ones at the practical job site.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.21 no.11
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• pp.2037-2042
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• 2017

The manufacturing facility is generally operated by a pre-set program under the existing factory automation system. On the other hand, the manufacturing facility must decide how to operate autonomously in Industry 4.0. Determining the operation mode of the production facility itself means, for example, that it detects the abnormality such as the deterioration of the facility at the shop-floor, prediction of the occurrence of the problem, detection of the defect of the product, In this paper, we propose a manufacturing process modeling using a queue for detection of manufacturing process abnormalities at the shop-floor, and detect abnormalities in the modeling using SVM, one of the machine learning techniques. The queue was used for M / D / 1 and the conveyor belt manufacturing system was modeled based on ${\mu}$, ${\lambda}$, and ${\rho}$. SVM was used to detect anomalous signs through changes in ${\rho}$.