A Multiresolution Approach to Model-Based 3-D Surface Quality Inspection
We propose a novel model-based surface approximation method for three-dimensional (3-D) surface quality inspection that combines a machine learning approach with multiresolution paradigms. Acceptable surface deviations are modeled by learning a number of 3-D measurements of tolerance samples. At the...
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Published in | IEEE transactions on industrial informatics Vol. 12; no. 4; pp. 1498 - 1507 |
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Main Authors | , |
Format | Journal Article |
Language | English |
Published |
Piscataway
IEEE
01.08.2016
The Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
Subjects | |
Online Access | Get full text |
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Summary: | We propose a novel model-based surface approximation method for three-dimensional (3-D) surface quality inspection that combines a machine learning approach with multiresolution paradigms. Acceptable surface deviations are modeled by learning a number of 3-D measurements of tolerance samples. At the same time, areas with high surface details are hierarchically refined, allowing an improved spatial localization of the surface model. The method is based on a dual eigenvalue decomposition, which leads to fast computation for large datasets of ordered 3-D point clouds. The proposed algorithm is easy to configure and requires few parameters by automatically determining the areas for local refinement. It yields a better defect detection on deformable parts and parts with high tolerance ranges, especially on critical areas with high surface curvature. Experimental results show the effectiveness compared to model-based approaches without multiresolution as well as nonmodel-based methods. Examples are given for successful defect detection where previous methods have failed. |
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Bibliography: | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 23 |
ISSN: | 1551-3203 1941-0050 |
DOI: | 10.1109/TII.2016.2585982 |