文献类型：期刊文献

英文题名：An AI-Powered Product Identity Form Design Method Based on Shape Grammar and Kansei Engineering: Integrating Midjourney and Grey-AHP-QFD

作者：Wang, Chenlu[1];Zhang, Jie[1];Liu, Dashuai[1];Cai, Yuchao[1];Gu, Quan[1]

机构：[1]East China Univ Sci & Technol, Sch Art Design & Media, Shanghai 200231, Peoples R China

年份：2024

卷号：14

期号：17

外文期刊名：APPLIED SCIENCES-BASEL

收录：;EI(收录号：20243817047638);WOS:【SCI-EXPANDED(收录号:WOS:001311079200001)】；

基金：This research was funded by the Major Project of the National Social Science Foundation of China (21 & ZD215); the National Social Science and Arts Foundation of China (20BH154); and the Shanghai Social Science Special Fund (2019ZJX002).

语种：英文

外文关键词：product form design; PI; SG; G-KE; G-QFD; Midjourney; E-moped

摘要：Product Identity (PI) is a strategic instrument for enterprises to forge brand strength through New Product Development (NPD). Concurrently, facing increasingly fierce market competition, the NPD for consumer emotional requirements (CRs) has become a significant objective in enterprise research and development (R&D). The design of new product forms must ensure the continuity of PI and concurrently address the emotional needs of users. It demands a high level of experience from designers and significant investment in R&D. To solve this problem, a generative and quantitative design method powered by AI, based on Shape Grammar (SG) and Kansei Engineering (KE), is proposed. The specific method is as follows: Firstly, representative products for Morphological Analysis (MA) are selected, SG is applied to establish initial shapes and transformation rules, and prompts are input into Midjourney. This process generates conceptual sketches and iteratively refines them, resulting in a set of conceptual sketches that preserve the PI. Secondly, a web crawler mines online reviews to extract Kansei words. Factor Analysis (FA) clusters them into Kansei factors, and the Grey Analytic Hierarchy Process (G-AHP) calculates their grey weights. Thirdly, after analyzing the PI conceptual sketches for feature extraction, the features are integrated with CRs into the Quality Function Deployment (QFD) matrix. Experts evaluate the relationships using interval grey numbers, calculating the optimal ranking of PI Engineering Characteristics (PIECs). Finally, professional designers refine the selected sketches into 3D models and detailed designs. Using a Chinese brand as a case study, we have designed a female electric moped (E-moped) to fit the PI and users' emotional needs. Through a questionnaire survey on the design scheme, we argue that the proposed innovative method is efficient, applicable, and effective in balancing the product form design of PI and user emotions.