Comparative analysis of AIGC-assisted and conventional design approaches in car seat design

Abstract

With the rapid advancement of artificial intelligence technology, the application of Artificial Intelligence Generated Content (AIGC) in the realms of creativity and design is becoming increasingly prevalent. This paper seeks to explore a comparative study between AIGC-assisted and traditional methods in automotive seat design. The design of automotive seats is a complex process that integrates considerations of ergonomics, material science, safety, and comfort. Traditional design approaches rely on the experience of designers and preliminary user research to iteratively refine design solutions, a process that is time-consuming and contingent upon the skill level of the designers. By utilizing the Pole Position (ABE) automotive seat design project as a case study and employing the SWOT analysis model, this research compares traditional design methods with those augmented by AIGC. The findings indicate that AIGC-assisted design excels in reducing design timeframes, enhancing design diversity, and increasing user satisfaction. However, traditional methods still hold an edge in deeply understanding and integrating user needs to achieve emotional design. Consequently, this study recommends integration of AIGC-assisted with traditional design approaches, leveraging the strengths of AI to supplement the deficiencies of conventional methods, and harnessing the creative thinking of designers to realize more humanized and personalized automotive seat designs.