A Review of Advances in Composite Materials, Structural Optimization, and Machine Learning for Wind Turbine Blades: Challenges and Future Perspectives

This paper reviews recent advancements across the lifecycle of wind turbine blades, focusing on three interconnected areas: advanced composites, structural optimization, and machine learning (ML) diagnostics. In materials, we highlight progress in hybrid carbon/glass/natural fiber orientations, advanced manufacturing scalability, and microcapsule self-healing matrices. In optimization, we evaluate multi-objective evolutionary algorithms (e.g., NSGA-II) that balance the conflict between aerodynamic efficiency and blade mass under strict deflection and fatigue constraints. In operational maintenance, we analyze deep learning frameworks (e.g., RNNs, Gaussian Processes) for real-time structural health monitoring and predictive icing detection under environmental variations. The academic value of this review lies in bridging the gap between multi-scale manufacturing defect physics and macro-level AI diagnostics. Industrially, it provides a concise roadmap for wind energy engineers to optimize structural reliability, enhance manufacturing yields, and extend the lifespan of next-generation megawatt-scale blades.