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Sustainability Assessment of Intelligent Manufacturing Supported by Digital Twin

Li, Lianhui (författare)
North Minzu Univ, Peoples R China; Jinan Univ, Peoples R China
Qu, Ting (författare)
Jinan Univ, Peoples R China
Liu, Yang (författare)
Linköpings universitet,Industriell miljöteknik,Tekniska fakulteten,Jinan Univ, Peoples R China
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Zhong, Ray Y. (författare)
Univ Hong Kong, Peoples R China
Xu, Guanying (författare)
China Mobile Commun Grp Design Inst Co Ltd, Peoples R China
Sun, Hongxia (författare)
North Minzu Univ, Peoples R China
Gao, Yang (författare)
North Minzu Univ, Peoples R China
Lei, Bingbing (författare)
North Minzu Univ, Peoples R China
Mao, Chunlei (författare)
Nanjing Hydraul Res Inst, Peoples R China
Pan, Yanghua (författare)
Macau Univ Sci & Technol, Peoples R China
Wang, Fuwei (författare)
North Minzu Univ, Peoples R China
Ma, Cong (författare)
North Minzu Univ, Peoples R China
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 (creator_code:org_t)
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC, 2020
2020
Engelska.
Ingår i: IEEE Access. - : IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC. - 2169-3536. ; 8, s. 174988-175008
  • Tidskriftsartikel (refereegranskat)
Abstract Ämnesord
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  • As 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.

Ämnesord

TEKNIK OCH TEKNOLOGIER  -- Elektroteknik och elektronik -- Datorsystem (hsv//swe)
ENGINEERING AND TECHNOLOGY  -- Electrical Engineering, Electronic Engineering, Information Engineering -- Computer Systems (hsv//eng)

Nyckelord

Digital twin; sustainability; intelligent manufacturing; fuzzy number; analytic hierarchy process; complex networks; PROMETHEE II; evidence theory

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