Full metadata record
DC Field | Value | Language |
---|---|---|
dc.contributor.author | 신승준 | - |
dc.date.accessioned | 2019-11-27T00:12:38Z | - |
dc.date.available | 2019-11-27T00:12:38Z | - |
dc.date.issued | 2017-07 | - |
dc.identifier.citation | INTERNATIONAL JOURNAL OF PRODUCTION RESEARCH, v. 55, no. 18, page. 5450-5464 | en_US |
dc.identifier.issn | 0020-7543 | - |
dc.identifier.issn | 1366-588X | - |
dc.identifier.uri | https://www.tandfonline.com/doi/full/10.1080/00207543.2017.1321799 | - |
dc.identifier.uri | https://repository.hanyang.ac.kr/handle/20.500.11754/114882 | - |
dc.description.abstract | Large manufacturers have been using simulation to support decision-making for design and production. However, with the advancement of technologies and the emergence of big data, simulation can be utilised to perform and support data analytics for associated performance gains. This requires not only significant model development expertise, but also huge data collection and analysis efforts. This paper presents an approach within the frameworks of Design Science Research Methodology and prototyping to address the challenge of increasing the use of modelling, simulation and data analytics in manufacturing via reduction of the development effort. The use of manufacturing simulation models is presented as data analytics applications themselves and for supporting other data analytics applications by serving as data generators and as a tool for validation. The virtual factory concept is presented as the vehicle for manufacturing modelling and simulation. Virtual factory goes beyond traditional simulation models of factories to include multi-resolution modelling capabilities and thus allowing analysis at varying levels of detail. A path is proposed for implementation of the virtual factory concept that builds on developments in technologies and standards. A virtual machine prototype is provided as a demonstration of the use of a virtual representation for manufacturing data analytics. | en_US |
dc.description.sponsorship | This work was supported by the United States Government; the cooperative agreement [grant number 70NANB13H158] between NIST and George Washington University; and NIST's Foreign Guest Researcher Program. This research was partially supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIP) [grant number 2016R1C1B1008820]. | en_US |
dc.language.iso | en_US | en_US |
dc.publisher | TAYLOR & FRANCIS LTD | en_US |
dc.subject | simulation applications | en_US |
dc.subject | performance analysis | en_US |
dc.subject | process modelling | en_US |
dc.subject | CNC machining | en_US |
dc.subject | production modelling | en_US |
dc.subject | virtual factory | en_US |
dc.subject | data analytics | en_US |
dc.title | Manufacturing data analytics using a virtual factory representation | en_US |
dc.type | Article | en_US |
dc.relation.no | 18 | - |
dc.relation.volume | 55 | - |
dc.identifier.doi | 10.1080/00207543.2017.1321799 | - |
dc.relation.page | 5450-5464 | - |
dc.relation.journal | INTERNATIONAL JOURNAL OF PRODUCTION RESEARCH | - |
dc.contributor.googleauthor | Jain, Sanjay | - |
dc.contributor.googleauthor | Shao, Guodong | - |
dc.contributor.googleauthor | Shin, Seung-Jun | - |
dc.relation.code | 2017002924 | - |
dc.sector.campus | S | - |
dc.sector.daehak | DIVISION OF INDUSTRIAL INFORMATION STUDIES[S] | - |
dc.sector.department | DIVISION OF INDUSTRIAL INFORMATION STUDIES | - |
dc.identifier.pid | sjshin | - |
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