We found a match
Your institution may have access to this item. Find your institution then sign in to continue.
- Title
An Efficient Product-Customization Framework Based on Multimodal Data under the Social Manufacturing Paradigm.
- Authors
Li, Yanpeng; Wu, Huaiyu; Tamir, Tariku Sinshaw; Shen, Zhen; Liu, Sheng; Hu, Bin; Xiong, Gang
- Abstract
With improvements in social productivity and technology, along with the popularity of the Internet, consumer demands are becoming increasingly personalized and diversified, promoting the transformation from mass customization to social manufacturing (SM). How to achieve efficient product customization remains a challenge. Massive multi-modal data, such as text and images, are generated during the manufacturing process. Based on the data, we can use large-scale pre-trained deep learning models and neural radiation field (NeRF) techniques to generate user-friendly 3D contents for 3D Printing. Furthermore, by the cloud computing technology, we can achieve more efficient SM operations. In this paper, we propose an efficient product-customization framework that can provide new ideas for the design, implementation, and optimization of collaborative production, and can provide insights for the upgrading of manufacturing industries.
- Subjects
DEEP learning; MULTIMODAL user interfaces; MASS customization; THREE-dimensional printing; CLOUD computing; MANUFACTURING processes; CONSUMPTION (Economics)
- Publication
Machines, 2023, Vol 11, Issue 2, p170
- ISSN
2075-1702
- Publication type
Article
- DOI
10.3390/machines11020170