The ability of AI to process vast datasets can enhance creativity, but its rigid knowledge base and lack of reflective thinking limit sustainable design. Generative Design Thinking (GDT) integrates human cognition and machine learning to enhance design automation. This study aims to explore the cognitive mechanisms underlying GDT and their impact on design efficiency. Using behavioral coding and quantitative analysis, we developed a three-tier cognitive model comprising a macro-cycle (knowledge acquisition and expression), meso-cycle (creative generation, intelligent evaluation, and feedback adjustment), and micro-cycle (knowledge base and model optimization). The findings reveal that increased task complexity elevates cognitive load, supporting the hypothesis that designers need to allocate more cognitive resources for complex problems. Knowledge base optimization significantly impacts design efficiency more than generative model refinement. Moreover, creative generation, evaluation, and feedback adjustment are interdependent, highlighting the importance of a dynamic knowledge base for creativity. This study challenges traditional design automation approaches by advocating for an adaptive framework that balances cognitive processes and machine capabilities. The results suggest that improving knowledge management and reducing cognitive load can enhance design outcomes. Future research should focus on developing flexible, real-time knowledge repositories and optimizing generative models for interdisciplinary and sustainable design contexts.

The increasing demand for efficiency, brand consistency, and sustainability in automotive design has led to the exploration of innovative methods. This study investigated the impact of the V-shaped Dynamic Morphology Curve (VDMC) on design efficiency, brand consistency, and sustainability outcomes in automobile wheel design. A total of 24 designers took part, divided into an experimental group using VDMC and a control group using traditional CAD methods. VDMC uses parametric modeling to accelerate design iterations while maintaining brand identity. The experimental group completed the design task 31.5% faster, achieved significantly higher brand consistency (9.1/10 vs. 7.8/10), and reduced the number of design iterations by 53.2% compared to the control group. Furthermore, the experimental group made 50.9% fewer design changes, indicating higher design stability. These results show that VDMC significantly improves design efficiency and sustainability by reducing both time and resource consumption while ensuring greater alignment with brand guidelines. This study highlights the potential of VDMC to transform traditional design practices and offers notable benefits for both creative processes and environmental impact. The results suggest that integrating VDMC into design workflows could lead to significant improvements in efficiency and sustainability in the automotive industry and beyond.

User preferences serve as a crucial dimension in describing the product style and its psychological experience. To assist designers in rapidly producing wheel designs that align with user preferences and enhance design efficiency by improving design transparency, this research establishes a user preference-based Automotive Wheel Hub Style Characterization Model (AWSCM). The model primarily consists of a style space, a design element space, and a matching relationship space. The style space is used to capture user preferences and product styles, the design element space for sample clustering and design element extraction, and the matching relationship space for calculating and analyzing the mathematical relationships between product styles and design elements. The model explores and reveals the logical and mathematical quantitative relationships among user preferences, product styles, and design elements. The results of controlled experiments indicate that the AWSCM has significant positive impacts on the design time and design quality. Particularly for non-professional designers, the AWSCM can help reduce the design time and enhance design comprehension, thereby improving design efficiency. This suggests that the AWSCM is beneficial for aiding designers in quickly generating reasonable designs in their design practice.