The traditional automotive design process follows a structured, linear workflow, progressing through fixed phases from research to validation. While effective in ensuring reliability, this approach often results in high costs, prolonged development times, and limited flexibility for late-stage modifications. This paper introduces a Circular Iterative Design (CID) methodology, integrating Virtual Reality (VR) and Generative Artificial Intelligence (AI) within the Industrial Design Structure (IDeS) to enhance efficiency, adaptability, and innovation. The proposed framework enables real-time design iteration, significantly reducing early-stage conceptualization time by allowing designers to rapidly transition between 3D gestural sketching and 2D refinement. Advanced AI tools further accelerate the process by automating rendering, generating design variations, and optimizing surfaces with minimal manual adjustments. Additionally, by leveraging a progressive 3D dataset (Core Model) that evolves from initial sketches to refined Class A surfaces, CID minimizes redundant efforts and enhances workflow continuity. Compared to traditional workflows, this approach can reduce reliance on physical prototypes, leading to a faster and more cost-effective design cycle, while also promoting sustainability by optimizing resource usage. The findings of this study demonstrate that integrating VR, AI, and structured methodologies into automotive design offers a scalable, collaborative, and highly efficient alternative to conventional processes.
Galiè et al. (Thu,) studied this question.