Traffic congestion and air pollution caused by rapid urbanization have emerged as critical challenges in metropolitan areas worldwide. Urban air mobility (UAM), particularly electric propulsion-based systems, has gained attention as a promising solution. For the successful commercialization of UAM, a lightweight airframe design with ensured structural integrity is essential. This study proposes an optimized lightweight design process that integrates automated composite manufacturing with artificial intelligence (AI)-based material property prediction. Finite-element analysis (FEA) was performed on glass fiber-, basalt fiber-, and carbon fiber-reinforced polymers under identical deformation conditions to derive design material properties in terms of elastic modulus and weight reduction. A large-scale dataset of fiber-reinforced plastics was established through an automated manufacturing process, and a deep learning regression model was developed using Altair AI Studio to predict mechanical properties under untested material and process conditions. The predicted properties were applied to a UAM fuselage model, and FEA results demonstrated that composite structures achieved equivalent or superior stiffness with up to 50% weight reduction compared to aluminum. In addition, inverse reserve factor (IRF) analysis confirmed structural safety, with all configurations maintaining IRF values below 1. The proposed AI-driven framework provides a scalable and data-driven lightweight design methodology applicable to next-generation UAM and advanced air mobility structures.
Son et al. (Mon,) studied this question.