Recent advancements in morphing applications: Architecture, artificial intelligence integration, challenges, and future trends-a comprehensive survey

This study provides a comprehensive review of recent advancements in aerospace morphing technologies, focusing on integrating artificial intelligence (AI) into morphing architectures. It emphasizes AI's pivotal role in optimizing these systems, particularly through machine learning (ML), deep learning (DL), and reinforcement learning (RL), to enhance real-time adaptability, performance, and efficiency. The review categorizes developments in smart materials, compliant mechanisms, and adaptive structures, offering a detailed analysis of their architectural foundations. It further examines AI-driven aerodynamic optimization and control systems, highlighting recent solutions to structural integrity, energy efficiency, and scalability challenges. Key contributions since 2020 are synthesized through a year-by-year analysis, offering a clear overview of the research landscape. The paper also addresses emerging challenges in aerospace morphing and proposes strategies to alleviate them. Recommendations for future advancements emphasize the integration of state-of-the-art technologies. By critically evaluating current capabilities and limitations, this review provides valuable insights for researchers and practitioners, identifying AI's transformative potential in morphing systems and outlining the technical challenges that must be addressed for future morphing aerospace applications.