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

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Title: The algorithm for denoising point clouds of annular forgings based on Grassmann manifold and density clustering.
Authors: Zhang, Yu-Cun1 (AUTHOR) zhangyc919@126.com, Wang, An1 (AUTHOR) 921024343@qq.com, Kong, Tao2 (AUTHOR) kongtao312@126.com, Fu, Xian-Bin2 (AUTHOR), Fang, Dong-Qing3 (AUTHOR)
Source: Measurement Science & Technology. Nov2024, Vol. 35 Issue 11, p1-12. 12p.
Subjects: Grassmann manifolds, Forging (Manufacturing process), Point cloud, Manufacturing processes, Noise
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. [ABSTRACT FROM AUTHOR]
Copyright of Measurement Science & Technology is the property of IOP Publishing and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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DbLabel: Engineering Source
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  Data: The algorithm for denoising point clouds of annular forgings based on Grassmann manifold and density clustering.
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  Data: <searchLink fieldCode="JN" term="%22Measurement+Science+%26+Technology%22">Measurement Science & Technology</searchLink>. Nov2024, Vol. 35 Issue 11, p1-12. 12p.
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  Data: 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. [ABSTRACT FROM AUTHOR]
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  Data: <i>Copyright of Measurement Science & Technology is the property of IOP Publishing and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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      – Type: doi
        Value: 10.1088/1361-6501/ad66f0
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      – Code: eng
        Text: English
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        PageCount: 12
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    Subjects:
      – SubjectFull: Grassmann manifolds
        Type: general
      – SubjectFull: Forging (Manufacturing process)
        Type: general
      – SubjectFull: Point cloud
        Type: general
      – SubjectFull: Manufacturing processes
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      – SubjectFull: Noise
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            NameFull: Zhang, Yu-Cun
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              M: 11
              Text: Nov2024
              Type: published
              Y: 2024
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