The algorithm for denoising point clouds of annular forgings based on Grassmann manifold and density clustering

Abstract

In the industrial sector, annular forgings serve as critical load-bearing components in mechanical 
equipment. During the production process, the precise measurement of the dimensional parameters of 
annular forgings is of paramount importance to ensure their quality and safety. However, owing to the 
influence of the measurement environment, the manufacturing process of annular forgings can introduce 
varying degrees of noise, resulting in inaccurate dimensional measurements. Therefore, researching 
methods for three-dimensional point cloud data to eliminate noise in annular forging point clouds is of 
significant importance for improving the accuracy of forging measurements. This paper presents a 
denoising approach for three-dimensional point cloud data of annular forgings based on Grassmann 
manifold and density clustering (GDAD). First, within the Grassmann manifold, the core points for 
density clustering are determined using density parameters. Second, density clustering is performed 
within the Grassmann manifold, with the Cauchy distance replacing the Euclidean distance to reduce the 
impact of noise and outliers on the analysis results. Finally, a search tree model was constructed to filter 
out incorrect point cloud clusters. The fusion of clustering results and the search tree model achieved
denoising of point cloud data. Simulation experiments on annular forgings demonstrate that GDAD 
effectively eliminates edge noise in annular forgings and performs well in denoising point-cloud models 
with varying levels of noise intensity