Data processing algorithm of cone-cylinder forgings process based on spectral graph theory and Hungarian matching

• Published in: Journal of instrumentation Vol. 19; no. 7; p. P07040
• Main Authors: Zhang, Yucun, Wang, Shijie, Li, Qun, Mi, Songtao
• Format: Journal Article
• Language: English
• Published: Bristol IOP Publishing 01.07.2024
• Subjects: Algorithms; Cylinders; Data processing; Data processing methods; Data reduction methods; Detector alignment and calibration methods (lasers, sources, particle-beams); Forgings; Graph matching; Graph theory; Manufacturing; Random walk
• ISSN: 1748-0221; 1748-0221
• DOI: 10.1088/1748-0221/19/07/P07040

Abstract This paper presents a novel data processing algorithm. This algorithm is used to solve the problem of incomplete and misaligned of point cloud data due to the complexity of nuclear power containment cone-cylinder forgings and the limitation of laser scanner. Based on spectral graph theory and Hungarian matching, this paper first introduces the lazy random walk, and point cloud state vector is calculated during the walk to judge the local information, thereby eliminate the influence of noise. Then, characteristic edges are extracted using spectral graph theory. Additionally, the feature descriptors are calculated and the cost matrix is constructed using the feature descriptors. The Hungarian algorithm is applied for feature matching, facilitating a coarse registration of the point clouds. Finally, the improved point-to-plane iteration closest point is used for fine registration to ensure accurate alignment between point clouds. The experimental results demonstrate the algorithm's effectiveness in the registration of point clouds for nuclear power containment cone-cylinder forgings.