Digital twin-driven green material optimal selection and evolution in product iterative design

Springer Science and Business Media LLC - Tập 11 - Trang 647-662 - 2023
Feng Xiang1,2, Ya-Dong Zhou1,2, Zhi Zhang1,2, Xiao-Fu Zou3, Fei Tao4, Ying Zuo4,5
1Key Laboratory of Metallurgical Equipment and Control Technology, Ministry of Education, Wuhan University of Science and Technology, Wuhan, People’s Republic of China
2Hubei Key Laboratory of Mechanical Transmission and Manufacturing Engineering, Wuhan University of Science and Technology, Wuhan, People’s Republic of China
3Institute of Artificial Intelligence, Beihang University, Beijing, People’s Republic of China
4School of Automation Science and Electrical Engineering, Beihang University, Beijing, People's Republic of China
5Research Institute for Frontier Science, Beihang University, Beijing, People’s Republic of China

Tóm tắt

In recent years, green concepts have been integrated into the product iterative design in the manufacturing field to address global competition and sustainability issues. However, previous efforts for green material optimal selection disregarded the interaction and fusion among physical entities, virtual models, and users, resulting in distortions and inaccuracies among user, physical entity, and virtual model such as inconsistency among the expected value, predicted simulation value, and actual performance value of evaluation indices. Therefore, this study proposes a digital twin-driven green material optimal selection and evolution method for product iterative design. Firstly, a novel framework is proposed. Subsequently, an analysis is carried out from six perspectives: the digital twin model construction for green material optimal selection, evolution mechanism of the digital twin model, multi-objective prediction and optimization, algorithm design, decision-making, and product function verification. Finally, taking the material selection of a shared bicycle frame as an example, the proposed method was verified by the prediction and iterative optimization of the carbon emission index.

Tài liệu tham khảo

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