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.
Issue Section:
Research Papers
Keywords:
surface monitoring,
anomaly classification,
tensor voting,
3D point cloud data,
product surface inspection,
inspection and quality control,
metrology,
sensing,
monitoring,
and diagnostics
Topics:
Tensors
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