Detection of inclusion by using 3D laser scanner in composite prepreg manufacturing technique using convolutional neural networks

Among different manufacturing techniques available for composite aircraft structures, prepreg-based manual layup is widely used. During the fabrication process, the protective films of the prepregs or other materials used in the process could get inside as a foreign object between the layers. The present method of finding the inclusions during the prepreg layup is by visual inspection in the cleanroom. Carrying out visual inspection is challenging as the layup is usually carried out on large surfaces and reflective by nature. This paper proposes a 3D laser scanner-based approach for the detection of inclusion on flat and curved surfaces. Using the portable laser scanner, the surfaces of each layer are scanned and compared the resulting point clouds using with a reference layer data. Thicknesses between two surfaces are computed with Cloud to Cloud, Mesh to Cloud and Hausdorff distance to enhance the visibility of inclusions. It was found that this approach could enhance the visibility of inclusions over 50 micron and above. These enhanced features are used to train a multiview convolutional neural network to mark the inclusion regions, which can aid the inspector to identify the inclusion regions in a fast and efficient way.

Automated summary

A new method using a 3D laser scanner for detecting foreign objects in composite aircraft structures is proposed in this paper, enhancing the visibility of foreign objects. By calculating thickness between layers and using a convolutional neural network to mark foreign object regions, inspectors are aided in quickly and efficiently identifying foreign objects.