Generative Artificial Intelligence Driven Product Form Innovation Design Method
Abstract
Generative artificial intelligence provides rapid visual exploration for product design, but prompt-driven image generation alone often produces attractive concepts with weak semantic control, limited geometric feasibility, or excessive similarity to existing products. This study proposes a generative artificial intelligence driven product form innovation design method that couples semantic requirement encoding, geometry-constrained diffusion generation, novelty-aware candidate selection, and designer-guided refinement. Product requirements are translated into weighted form semantics and structural constraints. Stable Diffusion XL, ControlNet, and IP-Adapter are then combined to generate candidates while preserving functional silhouette and reference-level style information. A multi-objective form innovation score integrates semantic consistency, geometric novelty, manufacturing feasibility, and intra-set diversity. The method was validated through a desktop air-purifier form design experiment involving 36 designers and 12 expert evaluators. The proposed method achieved a semantic alignment score of 0.812 ± 0.031, a geometric novelty score of 0.384 ± 0.026, and an expert innovation rating of 6.12 ± 0.41, while reducing concept-development time to 18.6 ± 3.4 min. The results indicate that controlled generative exploration can improve product-form innovation without sacrificing structural coherence or design feasibility