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

doi:10.1007/s40436-023-00450-4

Advances in Manufacturing ›› 2023, Vol. 11 ›› Issue (4): 647-662.doi: 10.1007/s40436-023-00450-4

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

Feng Xiang^1,2, Ya-Dong Zhou^1,2, Zhi Zhang^1,2, Xiao-Fu Zou³, Fei Tao⁴, Ying Zuo^4,5

1. Key Laboratory of Metallurgical Equipment and Control Technology, Ministry of Education, Wuhan University of Science and Technology, Wuhan, 430081, People's Republic of China;
2. Hubei Key Laboratory of Mechanical Transmission and Manufacturing Engineering, Wuhan University of Science and Technology, Wuhan, 430081, People's Republic of China;
3. Institute of Artificial Intelligence, Beihang University, Beijing, 100191, People's Republic of China;
4. School of Automation Science and Electrical Engineering, Beihang University, Beijing, 100191, People's Republic of China;
5. Research Institute for Frontier Science, Beihang University, Beijing, 100191, People's Republic of China

Received:2022-11-09 Revised:2023-01-02 Published:2023-10-27
Contact: Ying Zuo,E-mail:yingzuo@buaa.edu.cn E-mail:yingzuo@buaa.edu.cn
Supported by:
This work was supported by the National Natural Science Foundation of China (Grant Nos. 51975431 and 52005025), and the Fundamental Research Funds for the Central Universities (Grant No. 51705379) in China.

Abstract

Abstract: 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.

The full text can be downloaded at https://link.springer.com/article/10.1007/s40436-023-00450-4

Key words: Product iterative design, Digital twin (DT), Green material optimal selection, Evolution mechanism, Iterative optimization

Feng Xiang, Ya-Dong Zhou, Zhi Zhang, Xiao-Fu Zou, Fei Tao, Ying Zuo. Digital twin-driven green material optimal selection and evolution in product iterative design[J]. Advances in Manufacturing, 2023, 11(4): 647-662.

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

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