An Efficient Product-Customization Framework Based on Multimodal Data under the Social Manufacturing Paradigm

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.

Automated summary

With improvements in social productivity and technology, consumer demands for personalized and diversified products are increasing, promoting the shift from mass customization to social manufacturing (SM). However, achieving efficient product customization remains a challenge. This paper proposes an efficient product-customization framework using deep learning models and NeRF techniques to generate user-friendly 3D contents for 3D printing, coupled with cloud computing technology for more efficient SM operations. It provides new ideas for collaborative production and insights for the upgrading of manufacturing industries.