Imagine building a giant wind turbine blade like a high-tech LEGO set, but instead of people doing everything by hand, smart robots handle the heavy lifting and precise layering. AI cameras act like a digital inspector, spotting mistakes instantly so they can be fixed before the blade is finished. This makes the whole process faster, safer for workers, and much more precise.
Wind turbine blade production is currently too manual, leading to high ergonomic risks, aerosol exposure for workers, and inconsistent quality that slows down the transition to 500 GW wind capacity.
A semi-automated pilot line featuring AI defect detection, automated ply kitting, and intelligent pick-and-place systems. This includes a full-scale demonstrator on a 40m blade.
If you are a turbine manufacturer dealing with slow production cycles and manual errors — this project developed a semi-automated pilot line that targets a 50% cycle time reduction and 95% first-pass yield.
If you are a materials supplier dealing with poor fabric drapability in large parts — this project developed automated ply scarfing and advanced preforms to improve joint integrity and material use.
If you are a robotics firm dealing with the difficulty of placing large, flexible parts without distortion — this project developed intelligent pick-and-place systems for mould placement.
The project targets a unit cost saving of 5% or more through increased efficiency and reduced waste.
The innovations are being validated on a 40 m blade span of the V236 platform at Vestas’ Isle of Wight facility.
Based on available project data, the project will deliver 13 Key Exploitable Results and a roadmap for industrial deployment.
The project runs from May 2026 to April 2030, aiming to reach TRL 6 by the end of the period.
It integrates semi-automation, AI vision detection, and digital quality assurance into a scalable pilot line.
The consortium is highly industry-weighted with a 44% industry ratio, featuring 4 industrial partners including giants like Vestas and Owens Corning, alongside 2 SMEs. This balance, supported by 3 research organizations and 2 universities across 6 countries, ensures that the technical AI and robotics research is directly tied to commercial viability and large-scale manufacturing constraints.
Contact the Technical University of Denmark (DTU) regarding the SAMBA project coordination.
Contact SciTransfer to connect with the SAMBA consortium for licensing the 13 Key Exploitable Results.
