The Impact of Artificial Intelligence on the Generative Design and Cross-Domain Performance of Athletic Footwear

Abstract

The athletic footwear industry is shifting from conventional linear R&D pipelines to advanced digital workflows driven by artificial intelligence (AI). This paper evaluates how AI-driven tools optimize established manufacturing paradigms, focusing on the systemic transition within sports shoe development rather than isolated technical breakthroughs. However, conventional pipelines face severe bottlenecks, including low concept iteration efficiency, prolonged finite element simulation latencies, and high tooling costs that impede mass customization. To address these challenges, this study analyzes a triple-transformation methodology. First, Latent Diffusion Models and Kansei Engineering are applied to quantify consumer perception and accelerate conceptual design. Second, machine learning surrogate models replace time-consuming simulations for rapid biomechanical and topological optimization. Third, computer vision frameworks are integrated with additive manufacturing to bypass physical molds and streamline personalized production pipelines. The evaluation demonstrates that the integration of predictive algorithms and 3D printing shortens development cycles while achieving precise gait customization. In conclusion, human-machine collaboration will emerge as the dominant paradigm in footwear engineering, successfully balancing industrial economies of scale with absolute product personalization.