| contributor author | Du, Juan | |
| contributor author | Yan, Hao | |
| contributor author | Chang, Tzyy-Shuh | |
| contributor author | Shi, Jianjun | |
| date accessioned | 2022-05-08T08:19:49Z | |
| date available | 2022-05-08T08:19:49Z | |
| date copyright | 10/25/2021 12:00:00 AM | |
| date issued | 2021 | |
| identifier issn | 1087-1357 | |
| identifier other | manu_144_5_051005.pdf | |
| identifier uri | http://yetl.yabesh.ir/yetl1/handle/yetl/4283806 | |
| description 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. | |
| publisher | The American Society of Mechanical Engineers (ASME) | |
| title | A Tensor Voting-Based Surface Anomaly Classification Approach by Using 3D Point Cloud Data | |
| type | Journal Paper | |
| journal volume | 144 | |
| journal issue | 5 | |
| journal title | Journal of Manufacturing Science and Engineering | |
| identifier doi | 10.1115/1.4052660 | |
| journal fristpage | 51005-1 | |
| journal lastpage | 51005-12 | |
| page | 12 | |
| tree | Journal of Manufacturing Science and Engineering:;2021:;volume( 144 ):;issue: 005 | |
| contenttype | Fulltext | |
