## 5 Discussion
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Classification** & **Description** & **Authors** & **Methods** & **Analysis** \\ \hline PI design & The application of SG in 2D design & [PERSON] and [PERSON] [21] & SG/Parametric coding & Consistent branding, yet excessive rule parameters. \\  & The application of SG in 3D design & [PERSON] et al. [22] & SG/function lables & Consistent PI, yet excessive label rules. \\  & This current study & & SG/Midiourney/G-QFD/KE & Simpler rules, preserving PI design. \\ \hline KE design & Computer programming in KE & [PERSON] et al. [5] & DM/SVR/KE & Efficiently mining and filtering emotional needs, but requires a high level of computer programming skills. \\  & Intelligence algorithms in KE & [PERSON] [31] & SVR/RST/QFD/KE & Higher fitting accuracy of mapping, but lacks the ability to quickly generate a large number of sketches. \\  & This current study & & SG/Midiourney/G-QFD/KE & Effectively acquires and processes KE requirements, precisely capturing needs and performing sketch screening. \\ \hline AIQ design & AI application in the & & AI/Text-to-Image/ & Effective in architectural concept design, yet unexplored in product design. \\  & creative phase of architectural design & [PERSON] [24] & AI/Text-to-Image/ & Effective in architectural concept design, yet unexplored in product design. \\  & AI application in & [PERSON] et al. [23] & AIGC/ChatGPT/ & Effective in color design, but form design \\  & product color-matching design & [PERSON] et al. [23] & Midjourney & remains unexplored. \\  & This current study & & SG/Midiourney/G-QFD/KE & Effective application in product form design. \\ \hline \hline \end{tabular}
\end{table} 
Table 17: Summary of comparison with literature. pleteness and objectivity of the extraction of CRs. Meanwhile, the introduction of grey numbers into AHP and QFD effectively addresses the issues of subjectivity and fuzziness in the case of a limited number of experts for processing the weights of CRs and their relationship to PIECs, respectively. This leads to the identification of optimal PIECs that meet users' emotions, guiding the final NPD. 2. This study assists SMEs in quickly obtaining a large number of sketch alternatives that conform to PI elements and a complete and accurate understanding of user emotional needs, even when they lack the resources of senior designers and design experts, for refining the final design. This method not only meets the needs of enterprises for shaping brand influence but also effectively captures the minds of consumers, promoting the sustainable development of the enterprise. 3. The study explores the introduction of Midjourney into the real field of product design, combined with SG, to generate a large number of PI concept sketches. This process not only helps designers quickly obtain a multitude of ideas but also completely replaces the complex, inefficient, and highly limited human-driven creative phase, effectively enhancing the efficiency of ideation and significantly reducing the difficulty of the design process. Nevertheless, this study has some limitations and needs to be followed up. 1. AI-powered tools in the field of product design tend to have a high degree of randomness during the creative process. Even with the use of multiple prompt words to constrain the output, designers still need to manually process the pre-trained image sources, which reduces work efficiency. Future research could explore the integration of different AIGC tools with the methods of this study to enhance the efficiency of concept sketch generation. 2. This study only investigates the formal design of PI, while Color, Material, and Finishing (CMF) are also elements of the PI design system. Future research should include these aspects to enhance the comprehensiveness of this research method in the PI system design. 3. In empirical studies, only the side view is considered, while in reality, the product exists in 3D space. Therefore, it is necessary to conduct further research on other perspectives.