Catching Defects Early: AI Blade Inspection in Wind Turbine Manufacturing

The Cost of Missed Defects

Wind turbine blades are among the most critical components of a turbine, and they’re also among the most expensive. Stretching over 80 meters long and constructed from layers of fiberglass or carbon fiber composites, each blade undergoes a complex, multi-step manufacturing process. One of the most crucial inspection stages comes when blade half is exposed and ready for quality control before the two blade sides are bonded together.

At this stage, any missed defect — whether it’s a delamination, void, fiber wrinkle, or crack — can turn into a serious problem down the line. Missed defects that make it out of the factory can lead to field failures, warranty claims, and costly replacements (often running into hundreds of thousands of dollars per blade).

The cost of finding a defect in the field is orders of magnitude higher than catching it in the factory. That’s why detecting and addressing defects early, particularly in the post-cure inspection stage, is critical.

Traditional Approach: Manual Inspection

For years, blade manufacturers have relied heavily on manual visual inspection at this stage. Skilled quality inspectors physically enter the blade half and visually scan the interior surfaces for any signs of defects. They use tools like flashlights and markers to identify and document areas of concern.

While this approach can be effective, it’s also labor-intensive, inconsistent, and prone to human error:

• Inspectors can fatigue quickly in these environments, especially when working long hours.
• Subtle defects may be missed due to inadequate lighting, poor angles, or inexperience.
• Recording of defects is often manual and delayed, making it difficult to analyze patterns across blades or feed insights back to engineering and process teams.
• Inspection teams often apply defect criteria inconsistently, introducing subjectivity and variability that results in more missed defects.

The result? Defects are sometimes missed entirely or discovered too late in the production cycle — when rework is difficult or when blade halves have already been bonded.

Smarter Quality: Introducing Computer Vision

To address these challenges, some manufacturers are turning to computer vision and machine learning to augment or replace manual inspection during this critical stage. Systems like WorkWatch use cameras installed above the mold to capture high-resolution images of the surface immediately after debag. These images are then analyzed by ML models designed to identify specific types of defects.

This AI-powered inspection process offers significant advantages:

• Speed: Entire blade halves can be inspected in minutes, not hours.
• Accuracy: The model can be tuned with increased sensitivity and to error on the side of caution, ensuring fewer missed defects
• Consistency: Unlike humans, the system never tires, forgets, or overlooks subtle indicators.
• Integration: Detected defects can be automatically flagged to QA and engineering teams through real-time alerts, annotated dashboards, and integration with quality management systems.

The result is a more reliable, repeatable, and traceable inspection process, helping manufacturers catch more defects earlier, reduce costly rework, and ship higher-quality blades.

Should You Still Rely on Manual Inspection?

Manual inspection still has its place — especially for complex judgments or areas where tactile or experiential input matters. However, computer vision provides a powerful layer of automation and reliability that helps inspectors focus their attention on high-risk areas or ambiguous findings. A hybrid model proves the best results, where AI pre-screens the blade and flags regions for human review — a model that increases both efficiency and confidence.

Quality Starts with Visibility

Catching defects after the blade leaves the mold but before it’s bonded is one of the most important quality steps in the wind blade manufacturing process. Traditional manual inspection, while effective in some cases, is too variable and labor-intensive to keep up with the scale, complexity, and quality demands of modern blade production.

Computer vision systems like WorkWatch are helping manufacturers catch more defects, earlier, and with more confidence. By automatically analyzing blade images immediately after cure, these solutions reduce human error, speed up inspection, and empower QA and engineering teams with better data.

If you're a manufacturer looking to improve blade quality, reduce warranty risk, and stay competitive in a rapidly evolving market, it's time to reimagine inspection — not as a manual bottleneck, but as a smart, scalable process driven by AI.