@inproceedings{b78d855da60a47d2957096fd2af3ca56,
title = "A Data-driven Decision-making Approach for Complex Product Design Based on Deep Learning",
abstract = "Traditional complex product design methods rely too much on the designer's experience and lack methodology, so they are susceptible to subjective factors. It is easy to overlook some critical influencing factors. The big data generated in the design process contains much knowledge and provides a new perspective for decision-making. This paper proposes a data-driven decision-making approach for complex product design based on deep neural network. Correlation analysis is used to find the critical dimensions of big data that affect decision-making. The big data generated in the complex product design process is analyzed through the deep neural network, and the value of design variables can be predicted. Finally, an experiment was conducted with a complex aerospace product, which proved the validity and accuracy of the approach proposed in this paper.",
keywords = "big data, complex product, decision-making, deep neural learning",
author = "Zou Lai and Siqin Fu and Hang Yu and Shulin Lan and Chen Yang",
note = "Publisher Copyright: {\textcopyright} 2021 IEEE.; 24th IEEE International Conference on Computer Supported Cooperative Work in Design, CSCWD 2021 ; Conference date: 05-05-2021 Through 07-05-2021",
year = "2021",
month = may,
day = "5",
doi = "10.1109/CSCWD49262.2021.9437761",
language = "English",
series = "Proceedings of the 2021 IEEE 24th International Conference on Computer Supported Cooperative Work in Design, CSCWD 2021",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "238--243",
editor = "Weiming Shen and Jean-Paul Barthes and Junzhou Luo and Yanjun Shi and Jinghui Zhang",
booktitle = "Proceedings of the 2021 IEEE 24th International Conference on Computer Supported Cooperative Work in Design, CSCWD 2021",
address = "United States",
}