Wind blade manufacturers continue to rely heavily on manual processes, but they are developing technologies to automate various aspects of the wind blade manufacturing process: material handling, ply layup, vacuum infusion, assembly and finishing operations. Their goal is to lower cycle time and production costs, which will help to reduce the levelized cost of energy (LCOE) and make wind power more cost-competitive with other energy sources. However, one opportunity for cost savings through automation has slipped under the industry’s radar: in-process wind blade inspection.
It’s strange that manual in-process inspection represents current best practice for most composites manufacturing shops, and even more strange for giant wind blades. Think about it. Blades for commercial megawatt-rated wind turbines sometimes stretch the length of a football field or more. Imagine sending a couple trained inspectors to walk football field after football field, all day long, looking for small anomalies that rarely occur. A yard marker that is out of alignment. A patch of grass that is thinner than spec. A broken sprinkler head. Now imagine that any anomalies the inspector misses could delay the start of the game and cost heavily – reworking the field, verifying consistent watering has taken place, resodding. For this analogy to match wind blade manufacturing more closely, imagine that one overlooked anomaly has the potential to create catastrophic consequences – maybe a giant sinkhole opening up while the teams are on the field.
Absurd as this analogy may be, it makes the point: Why is an industry that is moving as expeditiously as possible toward automation leaving in-process inspection up to people and 19th century tools?
Automatic machine vision-based inspection has been used in industrial settings for more than 15 years. Granted, utility wind blades are at the upper end of the scale for the size of manufacturing works-in-progress (WIPs), and covering that much ground with a smart camera (the standard machine vision tool) is impractical. Likewise pan/tilt/zoom cameras, which generally don’t have lenses big enough or the optical engineering needed to capture and calibrate small features on large surfaces.
But just as wind blade manufacturers are overcoming size-related difficulties as they automate manufacturing processes, so too an inspection system designed for large WIPs is able to overcome size and effectively perform wind blade inspections.
It’s time to automate this currently error-prone and time-consuming process. This blog makes the case for automatic in-process inspection of wind turbine blades.
Wind blade manufacturing flaws: risky business
It’s a given that manufacturing defects in a wind blade diminish its performance and service life. But how much? Is it enough to justify a transition to automatic inspection? And how much will a blade’s manufacturing cost and a turbine’s LCOE be improved by the automatic system’s faster and more reliable wind blade inspection? Our investigation has shown that the impact of manufacturing defects on wind blades is considerable, and the savings from automatic in-process inspection, even more so.
Blade manufacturing flaws affect blade performance in varying ways:
- Porosity and voids in resin or adhesives create local regions where structural strength is lower than designed, and local concentrations of stress around these defects lead to crack formation and propagation.
- Missing or mis-located material and components make the as-built performance of a blade deviate unacceptably from the design model.
- Fiber misalignment, waviness and wrinkles reduce the blade’s strength. In fact, one study shows that wrinkles in a composite material may reduce compressive strength in a sandwich structure by up to 55%. Siemens Gamesa recently identified wrinkles in its blades as a key quality control issue that has caused the company to restrict sales of its 5.X turbine platforms.
- Debonds are starting points for crack propagation.
- Delamination, often caused by foreign object debris (FOD), shortens a blade’s service life, raises maintenance costs and has the potential to lead to catastrophic failure.
Though these flaws are infrequent, their consequences are high. And perfect inspections that catch all of these flaws are simply not in the realm of possibilities for human inspectors. Factors such as fatigue, distractedness and expectation bias cannot be trained out of a person.
Flaws that are detected during post-process non-destructive testing (NDT) may be corrected before the blade is installed on a turbine, but at this point, major rework is required, which delays delivery and adds demonstrably to blade manufacturing costs. Flaws that slip past all factory inspections and are present in deployed blades compromise the useful life of the blade and may cause actual failure. Additionally, blade size and the sometimes-remote location of deployed wind turbines make field assessment and repair highly challenging and costly. Maintenance and repair costs constitute a critical factor in LCOE, and so does the length of a blade’s service life.
How to inspect giant blades automatically
Since conventional smart cameras and pan/tilt/zoom camera systems cannot be applied to in-process inspection of giant wind blades effectively or efficiently, what is needed for wind blade manufacturing companies to transition to an automatic inspection system?
- It would have to capture small visible features and attributes across large surfaces.
- In the large bays where wind blades are fabricated, it would need to capture high-quality images from a substantial distance. Our contacts in the industry tell us that the camera may need to be positioned 50 feet away from the inspected surface (standoff distance).
- It would require reliable image analysis capabilities to ensure that anomalies and defects don’t evade detection.
- One of the most important performance characteristics is the quality of captured images, which serve as the data needed to train AI models for image analysis. The system must capture ultra-high resolution images to detect small flaws on very large surfaces.
Low hanging fruit for reducing wind blade costs
Here is why we think wind blade manufacturing is ready for automatic in-process inspection: All the functionality we just described is already available in a proven and mature system. “Technology transfer” usually refers to the advancement of innovations from university or R&D facilities to commercial enterprises, but it is also an apt descriptor for LASERVISION, our AI-driven large-field in-process inspection system. After all, LASERVISION recently achieved TRL-9 in aerospace applications, and the lessons learned with airplane wings and helicopter rotor blades will directly benefit wind blade manufacturers. You can review LASERVISION’s spec here.
The primary difference between LASERVISION’s aerospace applications and wind blade inspection is standoff distance. We are putting together a long-throw LASERVISION and have already demonstrated that the system will support the 50ft standoff distance needed in large wind blade bays, as the images here attest. A 50ft standoff distance gives LASERVISION a 50ft-by-50ft field of view on the windblade surface. It also means a large depth of field, so that features stay in focus across curved surfaces.
Because LASERVISION is already in proven commercial applications, early adopters in the wind industry will surely build a competitive advantage over other wind blade manufacturers. Aerospace composites companies have reported that manual inspection and repair time takes between one and two thirds of total cycle time. The savings in cycle time alone are worth the investment, and near-100% detection of flaws – at a point in the process when flaw correction costs the least – makes automatic inspection a highly profitable technology for the wind industry.
We are always on the lookout for manufacturers to partner with us. To learn more about LASERVISION and its potential to revolutionize wind blade manufacturing, contact us for a free online and onsite demonstration.