文献类型：期刊文献

英文题名：Big Data and AI-Driven Product Design: A Survey

作者：Quan, Huafeng Li, Shaobo Zeng, Changchang Wei, Hongjing Hu, Jianjun

第一作者：Quan, Huafeng

通信作者：Hu, JJ[1]

机构：[1]Guizhou Univ Finance & Econ, Coll Big Data & Stat, Guiyang 550050, Peoples R China;[2]Guizhou Univ, State Key Lab Publ Big Data, Guiyang 550025, Peoples R China;[3]Civil Aviat Flight Univ China, Sch Comp Sci, Guanghan 618307, Peoples R China;[4]Guizhou Inst Technol, Sch Mech Engn, Guiyang 550003, Peoples R China;[5]Univ South Carolina, Dept Comp Sci & Engn, Columbia, SC 29201 USA

第一机构：Guizhou Univ Finance & Econ, Coll Big Data & Stat, Guiyang 550050, Peoples R China

通信机构：corresponding author), Univ South Carolina, Dept Comp Sci & Engn, Columbia, SC 29201 USA.

年份：2023

卷号：13

期号：16

外文期刊名：APPLIED SCIENCES-BASEL

收录：;Scopus(收录号：2-s2.0-85169075354);WOS:【SCI-EXPANDED(收录号:WOS:001056354500001)】；

基金：This work was supported by Guizhou Provincial Basic Research Program (Natural Science) under grant No. ZK[2023]029 and Guizhou Provincial Department of Education Youth Science and Technology Talents Growth Project under grant No. KY[2022]209.

语种：英文

外文关键词：product design; big data; AI algorithm; AI-generated content; Kansei engineering; generative design

摘要：As living standards improve, modern products need to meet increasingly diversified and personalized user requirements. Traditional product design methods fall short due to their strong subjectivity, limited survey scope, lack of real-time data, and poor visual display. However, recent progress in big data and artificial intelligence (AI) are bringing a transformative big data and AI-driven product design methodology with a significant impact on many industries. Big data in the product lifecycle contains valuable information, such as customer preferences, market demands, product evaluation, and visual display: online product reviews reflect customer evaluations and requirements, while product images contain shape, color, and texture information that can inspire designers to quickly generate initial design schemes or even new product images. This survey provides a comprehensive review of big data and AI-driven product design, focusing on how big data of various modalities can be processed, analyzed, and exploited to aid product design using AI algorithms. It identifies the limitations of traditional product design methods and shows how textual, image, audio, and video data in product design cycles can be utilized to achieve much more intelligent product design. We finally discuss the major deficiencies of existing data-driven product design studies and outline promising future research directions and opportunities, aiming to draw increasing attention to modern AI-driven product design.