The companies have created a machine learning solution on Microsoft Azure, that uses a laser grid to show precisely where each fibreglass layer should be placed.
Siemens Gamesa has stated that errors in fibreglass size or placement raise costs and limit the number of blades it can produce in each period. It claims this machine learning and Internet of Things (IoT) approach will dramatically reduce errors in manufacturing and speed up the process.
Siemens Gamesa has implemented this data-driven approach on one of its production lines in Aalborg, Denmark, and is looking to extend it to its factories in Le Havre, France, and in Hull, UK.
Finn Mainstone, Senior Product Manager, explained: “Each turbine blade is custom designed by our engineers to precise specifications and any defects during the manufacturing process can result in complex, costly and time-consuming corrections.
“To avoid this situation, our teams see a laser grid displayed on top of each blade that shows them exactly where to place each fibreglass layer. Crucially, they can now get instant alerts if the solution detects any errors or abnormalities in the surface of the blade.”