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Article: Sustainability Assessment of Intelligent Manufacturing Supported by Digital Twin

TitleSustainability Assessment of Intelligent Manufacturing Supported by Digital Twin
Authors
KeywordsSustainable development
Manufacturing
Production
Decision making
Physical layer
Issue Date2020
PublisherInstitute of Electrical and Electronics Engineers (IEEE): OAJ. The Journal's web site is located at http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=6287639
Citation
IEEE Access, 2020, v. 8, p. 174988-175008 How to Cite?
AbstractAs a major challenge and opportunity for traditional manufacturing, intelligent manufacturing is facing the needs of sustainable development in future. Sustainability assessment undoubtedly plays a pivotal role for future development of intelligent manufacturing. Aiming at this, the paper presents the digital twin driven information architecture of sustainability assessment oriented for dynamic evolution under the whole life cycle based on the classic digital twin mapping system. The sustainability assessment method segment of the architecture includes indicator system building, indicator value determination, indicator importance degree determination and intelligent manufacturing project assessing. A novel approach for treating the ambiguity of expert' judgment in indicator value determination by introducing trapezoidal fuzzy number into analytic hierarchy process is proposed, while the complexity of the influence relationship among the indicators is processed by the integration of complex networks modeling and PROMETHEE II for the indicator importance degree determination. A two-stage evidence combination model based on evidence theory is built for intelligent manufacturing project assessing lastly. The presented digital-twin-driven information architecture and the sustainability assessment method is tested and validated on a study of sustainability assessment of 8 intelligent manufacturing projects of an air conditioning enterprise. The results of the presented method were validated by comparing them with the results of the fuzzy and rough extension of the PROMETHEE II, TOPSIS and VIKOR methods, indicator importance degree determining method by entropy and indicator value determining method by accurate expert scoring.
Persistent Identifierhttp://hdl.handle.net/10722/289715
ISSN
2019 Impact Factor: 3.745
2015 SCImago Journal Rankings: 0.947
ISI Accession Number ID

 

DC FieldValueLanguage
dc.contributor.authorLi, L-
dc.contributor.authorQu, T-
dc.contributor.authorLiu, Y-
dc.contributor.authorZhong, RY-
dc.contributor.authorXU, G-
dc.contributor.authorSUN, H-
dc.contributor.authorGAO, Y-
dc.contributor.authorLEI, B-
dc.contributor.authorMAO, C-
dc.contributor.authorPAN, Y-
dc.contributor.authorWANG, F-
dc.contributor.authorMA, C-
dc.date.accessioned2020-10-22T08:16:26Z-
dc.date.available2020-10-22T08:16:26Z-
dc.date.issued2020-
dc.identifier.citationIEEE Access, 2020, v. 8, p. 174988-175008-
dc.identifier.issn2169-3536-
dc.identifier.urihttp://hdl.handle.net/10722/289715-
dc.description.abstractAs a major challenge and opportunity for traditional manufacturing, intelligent manufacturing is facing the needs of sustainable development in future. Sustainability assessment undoubtedly plays a pivotal role for future development of intelligent manufacturing. Aiming at this, the paper presents the digital twin driven information architecture of sustainability assessment oriented for dynamic evolution under the whole life cycle based on the classic digital twin mapping system. The sustainability assessment method segment of the architecture includes indicator system building, indicator value determination, indicator importance degree determination and intelligent manufacturing project assessing. A novel approach for treating the ambiguity of expert' judgment in indicator value determination by introducing trapezoidal fuzzy number into analytic hierarchy process is proposed, while the complexity of the influence relationship among the indicators is processed by the integration of complex networks modeling and PROMETHEE II for the indicator importance degree determination. A two-stage evidence combination model based on evidence theory is built for intelligent manufacturing project assessing lastly. The presented digital-twin-driven information architecture and the sustainability assessment method is tested and validated on a study of sustainability assessment of 8 intelligent manufacturing projects of an air conditioning enterprise. The results of the presented method were validated by comparing them with the results of the fuzzy and rough extension of the PROMETHEE II, TOPSIS and VIKOR methods, indicator importance degree determining method by entropy and indicator value determining method by accurate expert scoring.-
dc.languageeng-
dc.publisherInstitute of Electrical and Electronics Engineers (IEEE): OAJ. The Journal's web site is located at http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=6287639-
dc.relation.ispartofIEEE Access-
dc.rightsIEEE Access. Copyright © Institute of Electrical and Electronics Engineers (IEEE): OAJ.-
dc.rightsThis work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.-
dc.subjectSustainable development-
dc.subjectManufacturing-
dc.subjectProduction-
dc.subjectDecision making-
dc.subjectPhysical layer-
dc.titleSustainability Assessment of Intelligent Manufacturing Supported by Digital Twin-
dc.typeArticle-
dc.identifier.emailZhong, RY: zhongzry@hku.hk-
dc.identifier.authorityZhong, RY=rp02116-
dc.description.naturepublished_or_final_version-
dc.identifier.doi10.1109/ACCESS.2020.3026541-
dc.identifier.hkuros315858-
dc.identifier.volume8-
dc.identifier.spage174988-
dc.identifier.epage175008-
dc.identifier.isiWOS:000575879900001-
dc.publisher.placeUnited States-
dc.identifier.issnl2169-3536-

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