中文版 | English
Title

Data-driven generative design for mass customization: A case study

Author
Corresponding AuthorTang, Yunlong; Xiong, Yi
Publication Years
2022-10-01
DOI
Source Title
ISSN
1474-0346
EISSN
1873-5320
Volume54
Abstract
Generative design provides a promising algorithmic solution for mass customization of products, improving both product variety and design efficiency. However, the current designer-driven generative design formulates the automated program in a manual manner and has insufficient ability to satisfy the diverse needs of individuals. In this work, we propose a data-driven generative design framework by integrating multiple types of data to improve the automation level and performance of detail design to boost design efficiency and improve user satisfaction. A computational workflow including automated shape synthesis and structure design methods is established. More specifically, existing designs selected based on user preferences are utilized in the shape synthesis for creating generative models. For structural design, user-product interaction data gathered by sensors are used as inputs for controlling the spatial distributions of heterogeneous lattice structures. Finally, the pro-posed concept and workflow are demonstrated with a bike saddle design with a personalized shape and inner structures to be manufactured with additive manufacturing.
Keywords
URL[Source Record]
Indexed By
Language
English
SUSTech Authorship
First ; Corresponding
Funding Project
[52105261] ; [2022A1515010316]
WOS Research Area
Computer Science ; Engineering
WOS Subject
Computer Science, Artificial Intelligence ; Engineering, Multidisciplinary
WOS Accession No
WOS:000886982200001
Publisher
ESI Research Field
ENGINEERING
Data Source
Web of Science
Citation statistics
Cited Times [WOS]:2
Document TypeJournal Article
Identifierhttp://kc.sustech.edu.cn/handle/2SGJ60CL/417043
DepartmentSchool of System Design and Intelligent Manufacturing
Affiliation
1.Southern Univ Sci & Technol, Sch Syst Design & Intelligent Mfg, Shenzhen 518055, Peoples R China
2.Guilin Univ Elect Technol, Sch Mech & Elect Engn, Guilin 541004, Peoples R China
3.Guilin Univ Elect Technol, Natl Demonstrat Ctr Expt Elect Circuit Educ, Guilin 541004, Peoples R China
4.Monash Univ, Mech & Aerosp Engn Dept, Melbourne 3168, Australia
First Author AffilicationSchool of System Design and Intelligent Manufacturing
Corresponding Author AffilicationSchool of System Design and Intelligent Manufacturing
First Author's First AffilicationSchool of System Design and Intelligent Manufacturing
Recommended Citation
GB/T 7714
Jiang, Zhoumingju,Wen, Hui,Han, Fred,et al. Data-driven generative design for mass customization: A case study[J]. ADVANCED ENGINEERING INFORMATICS,2022,54.
APA
Jiang, Zhoumingju,Wen, Hui,Han, Fred,Tang, Yunlong,&Xiong, Yi.(2022).Data-driven generative design for mass customization: A case study.ADVANCED ENGINEERING INFORMATICS,54.
MLA
Jiang, Zhoumingju,et al."Data-driven generative design for mass customization: A case study".ADVANCED ENGINEERING INFORMATICS 54(2022).
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