Sustainability Assessment of Intelligent Manufacturing Supported by Digital Twin

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. Funding Agencies|National Natural Science Foundation of ChinaNational Natural Science Foundation of China (NSFC) [51875251, 51765001]; Guangdong Special Support Talent Program-Innovation and Entrepreneurship Leading Team [2019BT02S593]; 2018 Guangzhou Leading Innovation Team Program (China) [201909010006]; Ningxia Natural Science Foundation [2020AAC03202, NZ17111]; Third Batch of Ningxia Youth Talents Supporting Program [TGJC2018048]; Blue Fire Project (Huizhou) Industry-University-Research Joint Innovation Fund of the Ministry of Education (China) [CXZJHZ201722]