A review of additive manufacturing techniques for wind turbine blade production: capabilities, AI integration, and Scale-Up Potential Sherif M. Hassanen, Yassmin Seid Ahmed, Belal Al Momani, Abbas Milani Frontiers in Mechanical Engineering, 2026 Hand lay-up and vacuum resin infusion are two labor-intensive, time-consuming, and expensive traditional manufacturing methods used for wind turbine (WT) blades. With the ability to reduce mold manufacturing costs by up to 50%, additive manufacturing (AM) has become an attractive alternative for blade tooling and component fabrication. In 2024, the global market for 3D-printed turbine components reached USD 1.2 billion and is expected to increase to USD 3.8 billion by 2033. This review investigates the integration of AM and artificial intelligence in WT blade manufacturing. AI-assisted defect detection has shown great accuracy in controlled experimental investigations, with some research showing classification accuracies exceeding 90% under controlled laboratory conditions. In several studies, multimodal sensing approaches outperformed single-sensor systems by around 20%. Furthermore, machine learning models have demonstrated excellent prediction ability for composite blade production quality in small-scale experimental datasets. While these findings are promising, further validation under full-scale industrial conditions is required. The synergy of artificial intelligence and additive manufacturing under Industry 4.0 can provide scalable, lightweight, sustainable production as well as enabling defect monitoring, optimization, and adaptive control. Moreover, this integration will improve sustainability through the use of recycled thermoplastic polymers as additive manufacturing feedstocks for blade tooling and small components, thereby reducing energy consumption and material waste compared to thermoset-based processes. However, current limitations include scalability constraints for blades beyond 12 m and a lack of standardized datasets. Research should focus on the development of hybrid artificial intelligence–additive manufacturing frameworks, digital-twin integration, and full-scale validation to accelerate the implementation of these technologies for wind turbine blade manufacturing.
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