Data-driven generative design for mass customization: A case study |
| |
| Affiliation: | 1. School of System Design and Intelligent Manufacturing, Southern University of Science and Technology, Shenzhen 518055, China;2. School of Mechanical and Electrical Engineering, and the National Demonstration Center for Experiment Electronic Circuit Education, Guilin University of Electronic Technology, Guilin 541004, China;3. Mechanical and Aerospace Engineering Department Monash University, Melbourne 3168, Australia;1. Center of Ultra-precision Optoelectronic Instrument Engineering, Harbin Institute of Technology, Harbin 150080, China;2. Key Lab of Ultra-precision Intelligent Instrumentation Engineering (Harbin Institute of Technology), Ministry of Industry and Information Technology, Harbin 150080, China;3. Institute of Reactor Operation and Application, Nuclear Power Institute of China, Chengdu 610000, China;1. School of Management, Northwestern Polytechnical University, Xi’an, PR China;2. Mechanical Engineering and Design Department, Université de Bourgogne Franche-Comté, Université de technologie de Belfort-Montbéliard, Belfort Cedex, France;3. School of Mechanical Engineering, Northwestern Polytechnical University, Xi’an, PR China;4. Guangdong Provincial Key Laboratory of Advanced Welding Technology for Ships, CSSC Huangpu Wenchong Shipbuilding Company Limited, Guangzhou, PR China;1. Department of Industrial Engineering, School of Mines, China University of Mining and Technology, Xuzhou 221116, Jiangsu, China;2. Department of Industrial Engineering, Business School, University of Shanghai for Science and Technology, Shanghai 200093, China;1. Key Laboratory of Industrial Engineering and Intelligent Manufacturing, School of Mechanical Engineering, Northwestern Polytechnical University, Xi''an, Shaanxi, PR China;2. College of Mechanical Engineering, Xi''an University of Science and Technology, Xi''an, Shaanxi, PR China;3. School of Mechanical and Aerospace Engineering, Nanyang Technological University, Singapore;4. Department of Industrial and manufacturing systems Engineering, The University of Hong Kong, Hong Kong |
| |
| 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 proposed concept and workflow are demonstrated with a bike saddle design with a personalized shape and inner structures to be manufactured with additive manufacturing. |
| |
| Keywords: | Design for additive manufacturing Generative design Data-driven design Mass customization Design automation Product design |
| 本文献已被 ScienceDirect 等数据库收录！ |
|
