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Study on the 3D-printed surface defect detection based on multi-row cyclic scanning method
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DOI：10.1016/j.measurement.2023.113823.png)
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摘要
En 中文
Defect detection is a crucial part of the additive manufacturing process. Based on 3D laser point cloud, this paper takes the surface of AM process as the research object for defect detection, including surface point cloud pre-treatment and internal defect detection. Aiming at a kind of interference existing on the boundary in the process of AM, we use continuous constraint and improved slope method to deal with it, and discuss two cases of continuous surface and non-continuous surface. The small threshold based pass-through filtering algorithm ex-tracts the external points of the defect boundary by using the fluctuation of the height value, and classifies the defect point cloud by classifying, combining and matching the external points. Finally, experimental verification results show that the accuracy of our proposed surface defect detection method can reach 92.3%. This method can effectively extract relatively complete defects. It provides important technical support for surface quality inspection in industrial automation.
Keyword:
Additive manufacturing
Pseudo points
Multi-row cycle
Scanline segmentation
Defects analysis and classification
期刊
IF:
5.6
论文数:
2.1W
被引数:
5.4W
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