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

• • 上一篇

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

收稿日期:2022-11-09 修回日期:2023-01-02 发布日期:2023-10-27
通讯作者: Ying Zuo,E-mail:yingzuo@buaa.edu.cn E-mail:yingzuo@buaa.edu.cn
作者简介:Feng Xiang received the B.S. and Ph.D. degrees in mechanical engineering from Wuhan University of Technology, Wuhan, China, in 2005 and 2013. He is currently a Professor with the School of Mechanical Automation, Wuhan University of Science and Technology, Wuhan, China. His research interests include digital twin, serviceoriented manufacturing, and green manufacturing.
Ya-Dong Zhou received the B.S. degree in industrial engineering from Wuhan University of Science and Technology, Wuhan, China in 2021, and is currently pursuing the M.S. degree in mechanical engineering, Wuhan University of Science and Technology, Wuhan, China. His research interests include digital twin, green manufacturing.
Zhi Zhang received the B.S. degree in Mechanical Engineering from Jiangxi University of Technology, Nanchang, China in 2017, M.S. degree in mechanical engineering from Wuhan University of Science and Technology, Wuhan, China in 2020. His research interests include digital twin, green manufacturing.
Xiao-Fu Zou received the Ph.D. degrees in Control Science and Engineering from Beihang University, Beijing, China, in 2019. He is currently an Associate Professor with the Institute of Artifcial Intelligence, Beihang University, Beijing, China. His research interests include digital twin, data processing, and embedded development.
Fei Tao received the B.S. and Ph.D. degrees in mechanical engineering from Wuhan University of Technology, Wuhan, China, in 2003 and 2008. He is currently a Professor with the School of Automation Science and Electrical Engineering, Beihang University, Beijing, China. He has authored five monographs and more than 100 journal papers in the areas of his current research interests, which include service-oriented smart manufacturing, manufacturing service management, and digital twin driven product design/manufacturing/service.
Ying Zuo received the B.S. degree in mechanical engineering and automation from Xi'an Polytechnic University, Xi'an, China, in 2009, M.S. degree in mechanical engineering from the Wuhan University of Technology, Wuhan, China, in 2012, and the Ph.D. degree in control science and engineering from Beihang University, Beijing, China, in 2017. He is currently an Assistant researcher at the Institute of Frontier Science and Technology Innovation, Beihang University. His research interests include service-oriented smart manufacturing, product energy-efciency evaluation and optimization, and manufacturing carbon neutralization.
基金资助:
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.

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.

摘要/Abstract

摘要： 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

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

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

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

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