Micro-structured films with surface riblets are used to reduce aerodynamic drag. This is especially relevant on fast and large objects such as on aircraft wings, where they are installed to increase efficiency (e.g., reduce fuel consumption). Their fuel reduction efficiency depends directly on the structural integrity of the films. Therefore, we propose a photometric inspection tool, a hardware setup and tailored analysis algorithms, which detect typical defects of riblet micro-structures occurring during their operational lifetime. We propose two inspection approaches to analyze the micro-structures, (i) a statistical data processing method and (ii) a machine learning algorithm based on convolutional autoencoders. We tested both inspection approaches on rendered and real world data of riblet films on airplane elements, carbon-fiber parts of race cars, and wind turbine blades.
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