• 제목/요약/키워드: Manufacturing process data

검색결과 1,603건 처리시간 0.03초

금형면 자동 다듬질 장치의 D/B 구축을 위한 실험적 연구 (Experimental Study of Developing D/B for Polishing Automation of Die and Mold)

  • 안유민
    • 한국생산제조학회지
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    • 제9권2호
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    • pp.80-86
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    • 2000
  • Although polishing process take 30-50% of whole process of manufacturing die and mold it has not been fully automat-ed yet. Considering current trend of manufacturing it is necessary to study on polishing automation. To accomplish automation reliable database must be developed. For developing it polishing mechanism should be defined and a general empirical formula that can be applied widely should be created. In this paper it is found that polishing process must be separated into 2 process such as removing cusp and getting fine surface process and the polishing parameter which is com-posed of major machining parameters and normalization of data can be applied efficiently in making reliable database.

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신경망 데이타 압축과 JPEG(표준정지영상압축기법)에 의한 원거리에 위치한 제조공정의 온라인 자동검사 (An Automatic On-Line Inspection of the Remotely Located Manufacturing Process Based on Neural Network Data Compression and Joint Photographic Experts Group)

  • 김상철;왕지남
    • 한국정밀공학회지
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    • 제13권2호
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    • pp.37-47
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    • 1996
  • This paper presents an automatic tele-inspection scheme for the remotely manufacturing process. The remote-manufacturing process is continuously monitored and a crucial process is captured by CCD Camera. The captured image is compressed by neural network and JPEG, and it is sent directly to the assembly plant for incoming inspection. Massive image data require broadband channel to transmit them to remote distance, but sender is able to transmit them to receiver in use common channel by compressing massive image data in the high ratio. After the receiver reconstructs the compressed image to be transmitted, the reconstructed image is also directly used for automatic inspection of the process. The Experimental results show that the proposed inspection mechanism could be effectively implemented for real applications.

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STEP AP224를 이용한 특징 형상의 가공 순서 계획 (Sequence Planning of Machining Features using STEP AP224)

  • 강무진
    • 한국CDE학회논문집
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    • 제9권2호
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    • pp.175-182
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    • 2004
  • As a bridge between design and manufacturing, process planning is to generate a sequenced set of instructions to manufacture the specified part. Automatic interpretation of manufacturing information incorporated in the design documentation such as CAD file has been a knotty subject for manufacturing engineers since no current data exchange format for product data provides a perfect interface between heterogeneous systems. The recent neutral data exchange format STEp, standard for the exchange of product model data, includes not only geometry but also technical and managerial information. STEP AP(Application Protocol) 224 is specifically dedicated to the mechanical product definition for process planning using machining features. Given a design information in STEP AP 224 format, process planning can be made without human intervention. This paper describes a method to determine the sequence of machining features by using the machining features and the manufacturing information expressed in STEP AP224.

Mining Information in Automated Relational Databases for Improving Reliability in Forest Products Manufacturing

  • Young, Timothy M.;Guess, Frank M.
    • 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.

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담배 제조 공정의 통계적 관리시스템 개발 (Development of Statistical Process Control System for Tobacco Manufacturing Process)

  • 김영호;송정호
    • 한국연초학회지
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    • 제23권1호
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    • pp.53-59
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    • 2001
  • To decrease of deviations from target specifications and excessive variability around targe, we exclusively designed statistical process control system involving general manager and expert tool for cigarette manufacturing process. This system is a unique programming environment for the development of total process control software including various control charts according to data type and process capability analysis. Also this system includes the statistical analysis module to analyze defective causes immediately when inferior products are made and the module to offer regular reports. This system is customized considering the manufacture environment based on the opinions of workers.

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제조 공정 빅데이터 분석을 위한 플랫폼 연구 (A Study on the Platform for Big Data Analysis of Manufacturing Process)

  • 구진희
    • 융합정보논문지
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    • 제7권5호
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    • pp.177-182
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    • 2017
  • IoT, 클라우드 컴퓨팅, 빅데이터와 같은 주요 ICT 기술이 제조 분야에 적용되기 시작하면서 스마트 공장 구축이 본격화 되고 있다. 스마트 공장 구현의 핵심은 공장 내외부의 데이터 확보 및 분석력에 있다. 따라서 빅데이터 분석 플랫폼에 대한 필요성이 증가하고 있다. 본 연구의 목적은 제조 공정 빅데이터 분석을 위한 플랫폼을 구성하고, 분석을 위한 통합 메소드를 제안하는데 있다. 제안하는 플랫폼은 대량의 데이터 셋을 분산 처리하기 위해 분석도구 R과 하둡을 통합한 RHadoop 기반 구조로서 자동화 시스템의 단위 공정 및 공장 내에서 수집되는 빅데이터를 하둡 HBase에 직접 저장 및 분석이 가능하다. 또한 기존 RDB 기반 분석의 한계점을 보완하였다. 이러한 플랫폼은 스마트 공장을 위한 단위 공정 적합성을 고려하여 개발되어야 하며, 제조 공정에 스마트 공장을 도입하고자 하는 중소기업에 IoT 플랫폼 구축의 가이드가 될 수 있을 것으로 전망된다.

데이터마이닝을 이용한 공정변수 확인 및 공정개선 (Identification Process Variables and Process Improvement Using Data Mining)

  • 정영수;강창욱;변성규
    • 산업경영시스템학회지
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    • 제28권3호
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    • pp.166-171
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    • 2005
  • With development of the database, there are too many data on process variables and the manufacturing process for the traditional statistical process control methods to identify the process variables related with assignable causes. Data mining is useful in this situation and provides variety of approaches for improving the process. In this paper, we applied control charts to monitor the process and if assignable causes are detected, then we applied the SVM technique and the sequence pattern analysis to find out the process variables suspected. These techniques made possible to predict the behavior of process variables. We illustrated our proposed methods with real manufacturing process data.

A Study on the Fault Process and Equipment Analysis of Plastic Ball Grid Array Manufacturing Using Data-Mining Techniques

  • Sim, Hyun Sik
    • Journal of Information Processing Systems
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    • 제16권6호
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    • pp.1271-1280
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    • 2020
  • The yield and quality of a micromanufacturing process are important management factors. In real-world situations, it is difficult to achieve a high yield from a manufacturing process because the products are produced through multiple nanoscale manufacturing processes. Therefore, it is necessary to identify the processes and equipment that lead to low yields. This paper proposes an analytical method to identify the processes and equipment that cause a defect in the plastic ball grid array (PBGA) during the manufacturing process using logistic regression and stepwise variable selection. The proposed method was tested with the lot trace records of a real work site. The records included the sequence of equipment that the lot had passed through and the number of faults of each type in the lot. We demonstrated that the test results reflect the real situation in a PBGA manufacturing process, and the major equipment parameters were then controlled to confirm the improvement in yield; the yield improved by approximately 20%.

빅데이터 도입을 위한 중소제조공정 4M 데이터 분석 (Data analysis of 4M data in small and medium enterprises)

  • 김재성;조완섭
    • 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.

다구찌방법을 이용한 사출성형공정의 신경회로망 모델링에 관한 연구 (A Study on Neural Network Modeling of Injection Molding Process Using Taguchi Method)

  • 최기흥;유병길;홍태민;이경돈;장낙영
    • 대한기계학회논문집A
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    • 제20권3호
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    • pp.765-774
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    • 1996
  • Computer Integrated Manufacturing(CIM) requires models of manufacturing processes to be implemented on the computer. These models are typically used for determining optimal process control parameters or designing adaptive control systems. In spite of the progress made in the mechanistic modeling, however, empirical models derived from experimental data play a maior role in manufacturing process modeling. This paper describes the development of a meural metwork medel for injection molding. This paper describes the development of a nueral network model for injection molding process. The model uses the CAE analysis data based on Taguchi method. The developed model is, then, compared with the traditional polynomial regression model to assess the applicabilit in practice.