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Currently, wind turbine blades are designed following a safe-life paradigm where manufacturing defects and operational damages are not taken into account. However, industry competition steers innovation towards typical solutions to maximize the economical output. The harsh environment, especially for offshore wind turbines, significantly degrades the turbine structural integrity and its performance. One of the major factors that lowers energy production due to aerodynamic losses is the erosion of the leading edges. Nowadays this erosion is monitored visually, either directly or with drone-based camera systems. We expect a quantitative characterization of the current erosion state and long term degradation monitoring to provide crucial information for wind farm maintenance and possible recoating of wind turbine blades. In this project, a customized long-range laser line scanner is used to measure the profiles of the blades at distances of up to 2 m. Individual scans are assembled into a 3D point cloud which is processed and analysed for defects and erosion. Further in the project, a custom-developed heavy drone will be used to scan the blades offshore. Current results include the detection of eroded areas, impact damages and surface defects in laboratory conditions with the sensor positioned with a scanning frame. Two processing approaches, namely deterministic and machine learning are compared and benchmarked with an ambition to perform erosion detection at a submillimeter scale. The deterministic approach includes local analysis of the surface variation, thresholding and clustering of the defects. Present controlled conditions allow investigation of how predicted drone vibrations, possible water droplets and salt contamination affect the inspection in a real offshore environment. This development is done within the smart maintenance innovation project AIRTuB: Automatic Inspection and Repair of Turbine Blades where the inspection expertise is combined with drone and wind-turbine manufacturers to change the turbine design paradigm from safe life to damage-tolerant.

Although the mask pattern created by fine ebeam writing is four times larger than the wafer pattern, the mask proximity effect from ebeam scattering and etch is not negligible. This mask proximity effect causes mask-CD errors and consequently wafer-CD errors after the lithographic process. It is therefore necessary to include the mask proximity effect in optical proximity correction (OPC). Without this, an OPC model can not predict the entire lithography process correctly even using advanced optical and resist models. In order to compensate for the mask proximity effect within OPC a special model is required along with changes to the OPC flow. This article presents a method for producing such a model and OPC flow and shows the difference in results when they are used.