A critical Review of Sustainable Additive Manufacturing Integrated Wind Power Systems: Configurations, Design Optimization, and Fault Detection AI Models

Wind turbine blades (WTBs) continue to increase in size and complexity, resulting in significant manufacturing and maintenance challenges. As WTBs are crucial and costly components, they may suffer material degradation and fatigue failure, which affects their performance and safety standards. Meanwhile, the failure data show that blade damage constitutes 19.40% of all turbine incidents, while leading-edge erosion (LEE) can appear as early as 1–2 years after installation. Additive manufacturing (AM) presents a scalable alternative, offering reduced lead time, on-site fabrication, and geometry freedom. Recent studies further indicate that AM can significantly reduce tooling costs, enhance fatigue performance of metallic components, and enable advanced design concepts. Furthermore, it supports predictive maintenance and digital-twin systems for real-time monitoring. This paper thoroughly reviews the state of these technologies, emphasizing how hybrid AM systems can improve performance, reduce blade waste, and enhance reliability throughout the blade lifecycle, while also identifying the remaining gaps in materials, process integration, and full lifecycle validation. Furthermore, the review also discusses AM with respect to maintenance, repair, and overhaul applications. Lastly, the paper analyzes the potential of artificial intelligence (AI) in wind energy, especially with respect to defect detection, additive manufacturing, wind power monitoring, and process control, thus enabling greater accuracy and efficient operation. Cooperatively, AM and AI are positioned as complementary enablers of a closed-loop, data-driven lifecycle for WTB design, manufacturing, operation, and maintenance.