An Improved Point Cloud Upsampling Algorithm for X-ray Diffraction on Thermal Coatings of Aeroengine Blades

Abstract

X-ray diffraction can non-destructively reveal microstructure information, including stress distribution on thermal coatings of aeroengine blades. In order to accurately pinpoint the detection position and precisely set the measurement geometry, a 3D camera is adopted to obtain the point cloud data on the blade surface and perform on-site modeling. Due to hardware limitations, the resolution of raw point clouds is insufficient. The point cloud needs to be upsampled. However, the current upsampling algorithm is greatly affected by noise and it is easy to generate too many outliers, which affects the quality of the generated point cloud. In this paper, a generative adversarial point cloud upsampling model is designed, which achieves better noise immunity by introducing dense graph convolution blocks in the discriminator. Additionally, filters are used to further process the noisy data before using the deep learning model. An evaluation of the network and a demonstration of the experiment show the effectivity of the new algorithm.