Detection and monitoring of defects on three-dimensional curved surfaces based on high-density point cloud data

Abstract

The surface quality of three-dimensional (3-D) curved surfaces is one of the most important factors that can directly influence the performance of the final product. This paper presents a systematic approach for detection and monitoring of defects on 3-D curved surfaces based on high-density point cloud data. Firstly, an algorithm to remove outliers and a boundary recognition algorithm are proposed to divide the entire 3-D curved surface including millions of measured points into multiple sub-regions. Secondly, two new evaluation indexes based on wavelet packet entropy and normal vector are explored to represent the features of the multiple sub-regions to determine whether the sub-regions are out-of-limit (OOL) of specifications. Thirdly, three quality parameters representing quality characteristics of a curved surface are presented and their values are calculated based on the clusters of OOL sub-regions. Finally, three individual control charts are presented to monitor the three quality parameters. As long as any quality parameter is out of the control range, the manufacturing process of the curved surface is determined to be out-of-control (OOC). The results of a case study show that the proposed approach can effectively identify the OOC manufacturing process and detect defects on 3-D curved surfaces.