Journal
CIRP ANNALS-MANUFACTURING TECHNOLOGY
Volume 67, Issue 1, Pages 145-148Publisher
ELSEVIER
DOI: 10.1016/j.cirp.2018.04.018
Keywords
Design; Customization; Deep learning
Funding
- Hong Kong Research Grants Council [UGC/FDS14/E02/15]
- Ministry of Science and Technology of Taiwan [MOST-106-2218-E-035-007]
Ask authors/readers for more resources
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. (C) 2018 Published by Elsevier Ltd on behalf of CIRP.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available