Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
8038674 | CIRP Annals - Manufacturing Technology | 2018 | 4 Pages |
Abstract
The key to successful product design is better understanding of customer needs (CNs), and efficiently translating CNs into design parameters (DPs). With the recent trend toward the diversification of CNs, the rapid introduction of new products, and shortened lead times, there is a growing need to speed up the mapping from CNs to DPs. By leveraging on product review data extracted e-commerce websites, this paper proposes a deep learning-based approach to improve the effectiveness and efficiency of mapping CNs to DPs. The results show that the proposed approach can meet customer needs with high efficiency.
Keywords
Related Topics
Physical Sciences and Engineering
Engineering
Industrial and Manufacturing Engineering
Authors
Yue Wang, Daniel Y. Mo, Mitchell M. Tseng,