Reducing defects in wind turbine blade manufacturing through systems integration
Wind energy capacity is scaling rapidly, driven by the global shift toward cleaner power. But as blade lengths now exceed 100 metres, manufacturing them at volume - without costly defects - remains a technical challenge.
The TURBO project (Towards Turbine Blade Production with Zero Waste), funded through Horizon Europe and Innovate UK, led by Siemens Gamesa Renewable Energy (SGRE), brought together 12 industrial and academic partners.
NCC was appointed as systems integrator – tasked with linking multiple advanced technologies into a robust, real-world manufacturing system.
This kind of work is increasingly critical as the UK moves to expand offshore wind infrastructure. Improving the reliability, efficiency, and scalability of wind turbine blade production is essential to delivering net zero at pace.
Challenge
In conventional production, nearly every wind turbine blade requires some degree of rework. Defects such as dry spots, delamination, or wrinkles are common, even in highly controlled environments. While manageable at small volumes, these issues become a major bottleneck as demand scales – adding cost, slowing delivery, and increasing waste.
The challenge was to reduce manufacturing defects by embedding sensor feedback, and adaptive control into the production process itself.
Approach
Rather than developing standalone technologies, NCC applied a systems engineering approach - combining materials science, digital engineering, and in-process monitoring into a single adaptive manufacturing platform.
Two integrated systems were developed:
Smart infusion control – A real-time monitoring and feedback system that tracks resin flow during casting and adjusts process parameters on the fly to prevent dry zones or voids.
Automated defect detection – A non-contact inspection system combining infrared thermography and optical coherence tomography to identify surface and internal defects early in the production cycle.
The approach was driven more by industrial need, than by academic interest. NCC worked closely with project partners to define requirements, map dependencies, and develop a technology integration roadmap grounded in real-world manufacturing conditions.
"Wind turbine blade manufacturing involves complex variable interactions that require adaptive systems to control. The TURBO framework identifies issues before they become defects - helping manufacturers de-risk innovation while scaling up production." - Dan Griffin, Principal Research Engineer, NCC
Results
The project delivered a fully functioning digital infrastructure to manage IoT data across the system - from sensors through processing, machine learning, and closed-loop control of the resin infusion process.
The integrated platform is now being transferred into SGRE’s development environment, to demonstrate how complex digital systems can be practically deployed at scale.
Impact
The approach is projected to reduce composite blade defects by up to 80%, with direct impacts on production efficiency, cost, and sustainability:
- Less rework and material waste
- Faster throughput and shorter cycle times
- Greater process confidence as volumes increase
"These efficiency improvements actually increase how many blades can be produced. Being green doesn't mean stopping growth." – Ben Clark, Advanced Technology Project Lead, NCC
Wider relevance
TURBO is more than a one-off technical success. It shows how digital transformation - when guided by applied systems engineering - can unlock reliability and scale in the manufacturing of large structures.
As the UK increases investment in domestic wind energy capability, this model provides a working example of what that transformation looks like: practical, scalable, and rooted in good engineering.
To learn more about how you can leverage this expertise, talk to our team.
AI controlled manufacturing
Resin infusion is hard to control and even harder to optimise for large structures. That’s where AI could make a real difference – but only if we build systems that engineers can trust. At NCC, we’ve created custom environments to train machine learning models on millions of infusion scenarios – pushing beyond what commercial tools can offer. Watch the video to learn more about how we're making it happen.
