Identification and extraction of surface defects on composite workpieces based on Multilayer Perceptron‐Moth Flame algorithm two‐stage neural network

Abstract In the aerospace sector, the identification and extraction of surface defects on composite parts is particularly important as they can have a very serious impact. This paper proposes a new extraction method for surface defects of composite materials, where 3D point clouds on the surface of composite workpieces are directly manipulated to finally extract a point cloud of defects containing both morphological and spatial location information. The specific operation explores the influencing factors in the formation and extraction process of composite surface defects and summarizes them into five categories: material type, fiber direction, curvature algorithm, neighborhood size, and filtering threshold. The Multilayer Perceptron‐Moth Flame algorithm two‐stage network model was constructed, which can reveal the relationship between these five types of influencing factors and the formation and extraction of surface defects in composites in the forward direction with an accuracy of 93.07%. It can also achieve optimal parameter recommendation in the reverse direction. Highlights Automatic identification and extraction of defects on the surface of composite workpieces is achieved based on 3D point cloud and curvature calculation. The forward direction reveals the relationship between the surface defect formation process and the material type and fabric direction. The reverse direction can automatically select the best curvature algorithm, neighborhood size, and filtering condition parameters. Build a Multilayer Perceptron‐Moth Flame Algorithm network structure based on multilayer perceptron and moth‐flame algorithm for model training of defect extraction, with an accuracy of 93.07%.