Surface delamination damage detection in CFRP/Ti stack drilling based on point cloud processing

Abstract

This paper focuses on the challenging problem of detecting the surface delamination damage during the drilling of carbon fiber reinforced polymer (CFRP). Considering the issues such as the low efficiency of traditional manual inspection, the limitations of non-destructive testing technologies, and the deficiencies of machine vision inspection algorithms, the point cloud processing technology is innovatively introduced. By analyzing the delamination damage mechanism and evaluation criteria, a drilling experiment is designed, and the point cloud data are collected. Voxel grid filtering and statistical filtering are applied to preprocess the point cloud. The moving least squares (MLS) method is adopted to smooth the point cloud. Based on the region growing algorithm and combined with the dual constraints of curvature and normal vector, the precise segmentation of the point cloud is realized. The experimental results demonstrate that the algorithm proposed in this paper achieves an accuracy rate of 91% for the detection of the surface delamination area with a repeatability accuracy of 1 mm², and an accuracy rate of 93% for the depth detection with a repeatability accuracy of 40 μm. This provides a novel solution for the detection of surface delamination damage during the drilling of CFRP.