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

Juan Du, Hao Yan, Tzyy-Shuh Chang, Jianjun Shi
May 2022
PDF Cite DOI

Abstract

Advanced three-dimensional (3D) scanning technology has been widely used to collect massive point cloud data for part dimension measurement and shape analysis. This paper presents a tensor voting-based approach for classifying surface anomalies on artifacts using 3D point cloud data, effectively addressing challenges related to data representation, high-dimensionality, and inconsistent sizes.

Type
Journal article
Publication
Journal of Manufacturing Science and Engineering
Surface Monitoring Anomaly Classification Tensor Voting 3D Point Cloud Data Quality Control
Juan Du
Juan Du
Assistant Professor

My research interests include knowledge-infused data science for quality improvement, industrial data analytics and machine learning, and system informatics and control for manufacturing applications.