@article{WANG2026, 
author = {Xiangzhang WANG and Lili YU},
title = {Variable fuzzy comprehensive evaluation for intelligent manufacturing digital twin model},
year = {2026},
journal = {Journal of Beijing University of Aeronautics and Astronautics},
volume = {52},
number = {1},
pages = {180-191},
keywords = {digital twin model, GQM method, information entropy, group decision, variable fuzzy recognition},
url = {https://www.sciopen.com/article/10.13700/j.bh.1001-5965.2023.0711},
doi = {10.13700/j.bh.1001-5965.2023.0711},
abstract = {A systematic multidimensional assessment index system for digital twin models was created utilizing the goal-question-metric (GQM) method in response to the dearth of reference standards and unified evaluation techniques for intelligent manufacturing digital twin models. By combining the advantages of variable fuzzy recognition model and information entropy aggregation weight algorithm, a digital twin quality value evaluation method based on improved variable fuzzy model was constructed. In order to solve the problems of fuzziness and uncertainty in expert evaluation, the variable fuzzy recognition model is improved by using group decision theory, and the index weight is calculated by using information entropy on the basis of considering expert opinion preference. Finally, the quality, performance and value of a digital twin model of an aircraft manufacturing plant are evaluated. According to the example study, the digital twin model of the aircraft production facility has an evaluation level of “S4 good,” although it tends to be “S3 qualified,” and there is still opportunity for improvement. At the same time, the feasibility and rationality of improving the variable fuzzy recognition model are verified, and the method support is provided for the construction of standardized intelligent manufacturing digital twin model.}
}