Scale and Pose-Invariant Feature Quality Inspection for Freeform Geometries in Additive Manufacturing
| Autor: | Harry A. Pierson, Haitao Liao, Yu Jin |
|---|---|
| Rok vydání: | 2019 |
| Předmět: |
0209 industrial biotechnology
Scale (ratio) Computer science business.industry Mechanical Engineering media_common.quotation_subject Invariant feature 020208 electrical & electronic engineering 02 engineering and technology Industrial and Manufacturing Engineering Computer Science Applications 020901 industrial engineering & automation Control and Systems Engineering 0202 electrical engineering electronic engineering information engineering Quality (business) Computer vision Artificial intelligence business media_common |
| Zdroj: | Journal of Manufacturing Science and Engineering. 141 |
| ISSN: | 1528-8935 1087-1357 |
| Popis: | Additive manufacturing (AM) has the unprecedented ability to create customized, complex, and nonparametric geometry, and it has made this ability accessible to individuals outside of traditional production environments. Geometric inspection technology, however, has yet to adapt to take full advantage of AM’s abilities. Coordinate measuring machines are accurate, but they are also slow, expensive to operate, and inaccessible to many AM users. On the other hand, 3D-scanners provide fast, high-density measurements, but there is a lack of feature-based analysis techniques for point cloud data. There exists a need for developing fast, feature-based geometric inspection techniques that can be implemented by users without specialized training in inspection according to geometric dimensioning and tolerancing conventions. This research proposes a new scale- and pose-invariant quality inspection method based on a novel location-orientation-shape (LOS) distribution derived from point cloud data. The key technique of the new method is to describe the shape and pose of key features via kernel density estimation and detect nonconformities based on statistical divergence. Numerical examples are provided and tests on physical AM builds are conducted to validate the method. The results show that the proposed inspection scheme is able to identify form, position, and orientation defects. The results also demonstrate how datum features can be incorporated into point cloud inspection, that datum features can be complex, nonparametric surfaces, and how the specification of datums can be more intuitive and meaningful, particularly for users without special training. |
| Databáze: | OpenAIRE |
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