Join leading companies like CarMax, Discount Tire, and Yamaha who are using Leverege to transform their real-world operations.
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Join leading companies like CarMax, Discount Tire, and Yamaha who are using Leverege to transform their real-world operations.
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Leading companies like TPI Composites rely on WorkWatch to improve production efficiency, security and safety with complete operational visibility.
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Leading companies like Discount Tire have implemented PitCrew in all their service centers to achieve maximum performance and throughput.
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Leading companies like Schnucks Markets have implemented ExpressLane wherever they have lines of people or vehicles, delighting customers with shorter wait times and faster service.
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Learn how AI validates ply accuracy in blade layup—cutting labor costs and improving quality control in wind turbine manufacturing.
In composite wind turbine blade manufacturing, it’s not just whether a ply is placed—it’s how precisely it’s placed. Each ply in a blade's laminate stack has a designated location and orientation, defined with tolerances measured in millimeters. These tolerances aren’t arbitrary; they’re engineered to ensure structural performance, fatigue resistance, and longevity in some of the most challenging operating environments in renewable energy.
Wind turbine blades must withstand millions of load cycles, including flapwise bending, torsional stress, and shear forces, especially in large offshore models where blades exceed 100 meters in length. Ply misplacement—even if only by a few centimeters—can shift the distribution of stresses through the laminate. Over time, that deviation can:
A well-placed ply contributes to a strong, predictable structure. A misaligned one introduces variability that’s difficult to catch after cure—and expensive to correct in the field.
To meet these tight tolerances, manufacturers rely heavily on a combination of skilled labor and tooling aids. Most commonly:
These methods, while effective to a point, come with trade-offs:
In aerospace and automotive composite manufacturing, automated fiber placement systems have become the gold standard for high-precision layup. AFP machines use robotic heads to place narrow tows of fiber with sub-millimeter accuracy, achieving exceptional control over fiber orientation and ply thickness.
The wind industry has explored AFP for blade layup, but progress has been slow, primarily due to a mismatch between the technology’s strengths and the realities of blade production:
As a result, while AFP research continues—particularly for hybrid or modular blade sections—it’s not yet a practical solution for mainstream ply placement in wind turbine blade manufacturing.
Computer vision, however, offers a promising middle ground. Rather than replacing human operators or reengineering the layup process entirely, AI-based vision systems can enhance current workflows by passively validating ply placement accuracy.
Here’s how it works:
This process requires no action from the operator, meaning there’s no interruption to production. The system passively validates that the ply was placed and placed correctly.
Precision in ply placement is non-negotiable in wind turbine blade manufacturing—but maintaining that precision at industrial scale is a constant challenge. Existing tools like laser projection and manual inspection do their job but rely heavily on labor, experience, and time.
Computer vision introduces a more scalable way to validate ply placement without disrupting production. By passively confirming both presence and tolerance using AI, manufacturers gain a new level of confidence and traceability—supporting structural performance and reducing the risk of costly rework or in-field failure.
At Leverege, we’ve developed WorkWatch, a computer vision tool designed to meet this need. WorkWatch uses overhead cameras and trained models to help teams monitor each ply for accuracy and completeness. It’s a practical step forward for manufacturers who want to enhance quality assurance without overhauling their process.
As blades grow and margins tighten, precision at every layer becomes even more critical. With the right tools, that precision becomes easier to achieve.