A review of AI-based product shape generation technologies: trends, challenges, and future directions

Background The rapid development of information technology has significantly propelled the integration and evolution of product design technologies and their related algorithms. This review systematically investigates the pivotal role of AI-driven product form generation technologies in promoting industrial design innovation and sustainable development. Methodology By employing bibliometric tools (Citespace) combined with visualization analysis, we propose a seven-stage technical framework encompassing “identification-extraction-analysis-generation-data mapping-decision-making-optimization.” Results The study traces the historical evolution, current research trends, and future development of product form generation design technologies. It highlights that artificial intelligence, as the core driving force, has substantially enhanced automated modeling and multi-objective optimization capabilities. However, challenges remain in areas such as data standardization deficits, limited dynamic adaptability, and insufficient cross-disciplinary collaboration. Future priorities should include: (1) strengthening algorithmic robustness to manage complex design scenarios; (2) integrating multimodal user feedback mechanisms to elevate interactive experiences; (3) constructing interpretable generative models to ensure design credibility; and (4) exploring green design-oriented intelligent algorithm deployment strategies with embedded ethical considerations.