Generative design of bamboo furniture combining game theory and AI-generated content

Consumers tend to purchase and use furniture products that fulfill their emotional needs. However, existing bamboo furniture design departments lack a systematic and scientific approach to morphological design, and their innovation capabilities remain insufficient. This study proposes a generative design method for bamboo furniture that integrates Game Theory (GT) with AI-Generated Content (AIGC), grounded in Kansei Engineering. This approach aims to assist design departments in developing creative products that align with consumers’ emotional needs, thereby fostering sustainable consumption and advancing the bamboo furniture industry. First, consumer-driven Kansei words were collected and categorized. Then, subjective and objective weight values of consumer requirements were calculated using Grey Relational Analysis (GRA) and entropy, respectively. Based on GT, a comprehensive weight value was determined to accurately identify key consumer requirements. Next, Diffusion Models in AIGC technology were employed to generate new furniture images, followed by morphological deconstruction. Finally, a House of Quality based on Fuzzy Quality Function Deployment was constructed to establish the mapping relationship between key consumer requirements and new morphological elements, determining the optimal furniture design parameters. The proposed method integrates the strengths of both subjective and objective approaches, enhancing the accuracy and scientific rigor of design decision-making.