• 한국정보통신학회논문지
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• 제26권2호
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• pp.199-206
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• 2022

제품을 생산하는 설비의 고장이나 이상 현상은 곧 제품의 결함 및 생산라인 가동 중단으로 이어져 제조 업체의 막대한 경제적 손실의 원인이 된다. 스마트팩토리 서비스의 확산으로 공장에서 많은 양의 데이터가 수집됨에 따라, 이를 활용하여 제조 현장의 효율이나 제조 설비의 고장 예측 및 진단을 위한 인공지능 기반의 연구가 활발히 이어지고 있다. 하지만 정상과 이상을 구분 짓는 레이블 정보가 명확하지 않고 이상에 대한 극심한 클래스 불균형을 가지는 제조 데이터의 특징으로 인하여 분류 모델이나 이상탐지 모델의 개발에는 큰 어려움이 존재한다. 본 논문에서는 딥러닝 모델의 재구성 손실값을 이용하여 제조 설비의 이상탐지를 위한 딥러닝 알고리즘을 제안하고 성능을 분석하였다. 해당 알고리즘은 이상 데이터를 제외한 설비의 제조 데이터, 즉 정상 데이터에만 의존하여 이상을 감지한다.

• 품질경영학회지
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• 제30권2호
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• pp.72-85
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• 2002

Advances in computer and sensor technology have made it possible to obtain superlarge manufacturing process data in real time, letting us extract meaningful information from these superlarge data sets. We propose a systematic data analysis procedure which field engineers can apply easily to manufacture quality products. The procedure consists of data cleaning and data analysis stages. Data cleaning stage is to construct a database suitable for statistical analysis from the original superlarge manufacturing process data. In the data analysis stage, we suggest a graphical easy-to-implement approach to extract practical information from the cleaned database. This study will help manufacturing companies to achieve six sigma quality.

• 대한기계학회:학술대회논문집
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• 대한기계학회 2001년도 춘계학술대회논문집A
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• pp.740-745
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• 2001

The plastic deformation behavior of formed CICC fur the superconducting Tokamac fusion device was examined and appropriate manufacturing information was provided. A relation between travel of the bending roller and spring back displacement was obtained via virtual manufacturing. The radius of CICC after forming was expressed as a function of the bend-roll travel. The maximum von Mises stress after spring back was also monitored fur the SAGBO prediction. Next, the variation of the CICC cross-sectional area was examined during the first turn and during conduit bending with the largest curvature. Finally, the coil radius was measured and compared with the data generated from the virtual manufacturing. The measured data showed similar pattern as predicted one. Using the mapping function found to match with the real data, the data from the virtual manufacturing may facilitate accurate manufacturing.

• 한국경영과학회지
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• 제17권2호
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• pp.25-33
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• 1992

Manufacturing cell formation is one of the most important problems faced in designing cellular manufacturing systems. The purpose of this study is to design effective manufacturing cell systems by developing a method which forms machines/parts into optimal machine cells/part families. The 0-1 data matrix structure is used to form a basis for manufacturing cell formation. In this paper, we propose a CE method to reorder the 0-1 data matrix for manufacturing cell formation. The resulting solutions are shown to demonstrate the effectiveness of the CE method.

• 디지털융복합연구
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• 제12권1호
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• pp.305-311
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• 2014

제조업에서는 경쟁우위 확보를 위해 일찍이 설계, 생산 과정의 자동화와 정보시스템을 도입하였다. 대표적인 정보시스템 중 하나가 제조실행시스템(Manufacturing Execution System)인데, 이러한 제조실행시스템은 진화를 거듭해 왔다. 최근 빅데이터가 등장하면서 MES도 빅데이터 적용 방안이 모색되고 있다. 이에 본 연구는 먼저 제조 분야에서의 빅데이터 활용에 대한 선행 연구 및 사례 분석을 토대로 MES에의 빅데이터 적용모델을 제안할 것이다.

• Biomaterials and Biomechanics in Bioengineering
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• 제3권1호
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• pp.1-14
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• 2016

Well-ordered porous materials are very promising in orthopedics since they allow tailoring the mechanical properties. Finite element (FE) analysis is commonly used to evaluate the mechanical behavior of well-ordered porous materials. However, FE results generally differ importantly from experimental data. In the present article, three types of manufacturing irregularities were characterized on an additive manufactured porous titanium sample having a simple cubic unit-cell: strut diameter variation, strut inclination and fractured struts. These were included in a beam FE model. Results were compared with experimental data in terms of the apparent elastic modulus (Eap) and apparent yield strength (SY,ap). The combination of manufacturing irregularities that yielded the closest results to experimental data was determined. The idealized FE model resulted in an Eap one order of magnitude larger than experimental data and a SY,ap almost twice the experimental values. The strut inclination and fractured struts showed the strongest effects on Eap and SY,ap, respectively. Combining the three manufacturing irregularities produced the closest results to experimental data. The model also performed well when applied to samples having different structural dimensions. We recommend including the three proposed manufacturing irregularities in the FE models to predict the mechanical behavior of such porous structures.

• 한국CDE학회논문집
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• 제21권3호
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• pp.215-223
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• 2016

The low price around 1000 USD makes consumer 3D printers as a new additive manufacturing platform for the personal manufacturing where consumers can make and sell their own products. To allow the consumers to design and manufacture their products, not only economic 3D printers but also supporting information systems for their design and manufacturing are essential. This study suggests an extended product data management (PDM) system that can support both the design and manufacturing of personal products with consumer 3D printing services. This extended PDM system helps consumer designers use advanced PDM technologies for their design and connected 3D printing services with Internet of Things (IoT) technology for realization of their products. As a result, the proposed system supports the consumer designers a seamless integrated product development and manufacturing environment supported by PDM and consumer 3D printing services.

• International Journal of Reliability and Applications
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• 제3권4호
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• pp.155-164
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• 2002

This paper focuses on how modem data mining can be integrated with real-time relational databases and commercial data warehouses to improve reliability in real-time. An important Issue for many manufacturers is the development of relational databases that link key product attributes with real-time process parameters. Helpful data for key product attributes in manufacturing may be derived from destructive reliability testing. Destructive samples are taken at periodic time intervals during manufacturing, which might create a long time-gap between key product attributes and real-time process data. A case study is briefly summarized for the medium density fiberboard (MDF) industry. MDF is a wood composite that is used extensively by the home building and furniture manufacturing industries around the world. The cost of unacceptable MDF was as large as 5% to 10% of total manufacturing costs. Prevention can result In millions of US dollars saved by using better Information systems.

• 한국경영과학회:학술대회논문집
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• 한국경영과학회 2001년도 추계학술대회 논문집
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• pp.411-415
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• 2001

Advances in computer and sensor technology have made it possible to obtain superlarge manufacturing process data in real time, letting us to extract meaningful information from these superlarge data sets. We propose a systematic data analysis procedure which field engineers can apply easily to manufacture quality products. The procedure consists of data cleaning and data analysis stages. Data cleaning stage is to construct a database suitable for statistical analysis from the original superlarge manufacturing process data. In the data analysis stage, we suggest a graphical easy-to-implement approach to extract practical information from the cleaned database. This study will help manufacturing companies to achieve six sigma quality.

• Journal of the Korean Data and Information Science Society
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• 제26권5호
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• pp.1117-1128
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• 2015

오늘날 ICT기술의 눈부신 발전으로 많은 부분에 정보화와 자동화가 이루어져 있으며, 제조업에서도 경쟁우위를 확보하기 위해 설계, 생산 공정의 자동화와 정보시스템을 도입하고 있다. 그러나 정보화 투자 여력이 없는 영세 중소제조 기업의 경우 생산현장에서 정보화의 힘이 미치지 못하고 있으며, 작업자의 경험과 수기데이터에 의존하여 생산 공정을 관리하고 있는 실정이다. 수기데이터로 관리되고 있는 제조공정에서는 불량 발생 시 불량원인을 명확히 밝혀내는데 한계가 있다. 본 연구에서는 수기데이터로 관리되고 있는 중소제조 자동차 부품 가공공정에 대하여, 수기데이터를 수집, 향후 센서데이터를 활용할 수 있도록 중소 제조 맞춤형 분석시스템을 구축하고, 중요도가 큰 일부 공정에 대하여 품질에 영향을 미치는 핵심요인을 4M관점에서 분석하였다. 분석결과, 호기별 불량수량에는 유의한 차이가 없었으며, 원자재, 생산수량, 작업자간 유의한 차이가 있는 것으로 분석되었다.