A Tensor Voting-Based Surface Anomaly Classification Approach by Using 3D Point Cloud Data

Juan Du, Hao Yan, Tzyy Shuh Chang, Jianjun Shi

Research output: Contribution to journalArticlepeer-review

Abstract

Advanced three-dimensional (3D) scanning technology has been widely used in many industries to collect the massive point cloud data of artifacts for part dimension measurement and shape analysis. Though point cloud data has product surface quality information, it is challenging to conduct effective surface anomaly classification due to the complex data representation, high-dimensionality, and inconsistent size of the 3D point cloud data within each sample. To deal with these challenges, this paper proposes a tensor voting-based approach for anomaly classification of artifact surfaces. A case study based on 3D scanned data obtained from a manufacturing plant shows the effectiveness of the proposed method.

Original languageEnglish (US)
Article number051005
JournalJournal of Manufacturing Science and Engineering, Transactions of the ASME
Volume144
Issue number5
DOIs
StatePublished - May 2022
Externally publishedYes

Keywords

  • 3D point cloud data
  • and diagnostics
  • anomaly classification
  • inspection and quality control
  • metrology
  • monitoring
  • product surface inspection
  • sensing
  • surface monitoring
  • tensor voting

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Mechanical Engineering
  • Computer Science Applications
  • Industrial and Manufacturing Engineering

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