Exploring synergies between AIGC and TRIZ in the optimisation of road cone design through integrated innovation methods

Abstract

Utilising the Theory of Inventive Problem Solving (TRIZ) and Artificial Intelligence Generated Content (AIGC) models, this paper explores AI-assisted creative design and uses traffic cone design as a case. The objective is to evaluate whether AIGC can assist or potentially replace TRIZ in this field. Despite TRIZ being an established methodology renowned for its effectiveness in systematic problem solving, it faces challenges related to implementation complexity and adaptability across different cultures and industries. In contrast, AIGC, through its data-driven tools, offers automation and enhanced innovation, showing promise to overcome these limitations. Evidence from qualitative case studies and quantitative studies indicate that while AIGC enhances the efficiency and stability of creative models, its limitations in system analysis and innovative thinking prevent it from independently solving complex creative design issues, thereby not fully replacing TRIZ. However, AIGC can effectively complement the TRIZ model by enhancing the practicality of solutions through its data processing capabilities, thereby improving overall design efficiency and innovation. This synergy has the potential to accelerate the development of innovative solutions and conceptual designs, indicating that integrating AIGC with TRIZ leverages the strengths of both methodologies to produce more effective and innovative design outcomes.

KEYWORDS:

• AIGC
• TRIZ
• design process optimisation
• collaborative innovation design

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Funding

This work was supported by Faculty Research Grants (FRG) of Macau University of Science and Technology [Grant Number FRG-22-098-FA-002A].