Performance evaluation of CNN and R-CNN based line by line analysis algorithms for fibre placement defect classification

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Abstract
The Automated Fibre Placement process is commonly used in aerospace for the manufacturing of structural components, but requires a subsequent inspection to meet the corresponding safety requirements. In order to improve this mostly manual inspection step, machine learning methods for the interpretation of 2D surface images are being increasingly utilised in research. Depending on the manufacturing process, a very long time can elapse between the appearance of a manufacturing defect and its recognition. Hence, in this paper Convolutional and Recurrent Neural Network techniques are presented that allow a line-by-line analysis of the incoming height profile scans of a Laser Line Scan Sensor as a 1D signal, which enables a direct reaction to a defect, even if only one or a few individual height profiles of the defect have been recorded. The combination of Convolutional and Recurrent Neural Network structures is particularly beneficial for this application. The investigations in this paper are especially interesting for developers of automated inspection systems in composite engineering.
Funding Information
  • Investment and Development Bank of Lower Saxony - NBank (ZW180159715)
  • Deutsches Zentrum für Luft- und Raumfahrt e. V. (DLR)

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