Global and local defect detection for 3D printout surface based on geometric shape comparison
A 3D printer produces parts from CAD models. The printout must be checked for defects. We present a surface defect detection algorithm that constructs a 3D mesh of the printout by multi-view scanning and compares it to the part CAD model. It computes and displays the distance from each vertex of the...
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| Published in | Precision engineering Vol. 82; pp. 324 - 337 |
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| Main Authors | , , |
| Format | Journal Article |
| Language | English |
| Published |
Elsevier Inc
01.07.2023
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| Subjects | |
| Online Access | Get full text |
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| Abstract | A 3D printer produces parts from CAD models. The printout must be checked for defects. We present a surface defect detection algorithm that constructs a 3D mesh of the printout by multi-view scanning and compares it to the part CAD model. It computes and displays the distance from each vertex of the printout mesh to the closest point on the part model. These distances comprise the global deviation of the printout from the model. When the error is within a threshold, the algorithm compares the curvature at each printout vertex to that at the closest part point. Qualitative differences in curvature indicate local defects, such as blobs, cracks, surface roughness, and faded embossing and engraving. The results are grouped into local defect regions that are displayed. We demonstrate the algorithm on metal printing and on PLA 3D printing. |
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| AbstractList | A 3D printer produces parts from CAD models. The printout must be checked for defects. We present a surface defect detection algorithm that constructs a 3D mesh of the printout by multi-view scanning and compares it to the part CAD model. It computes and displays the distance from each vertex of the printout mesh to the closest point on the part model. These distances comprise the global deviation of the printout from the model. When the error is within a threshold, the algorithm compares the curvature at each printout vertex to that at the closest part point. Qualitative differences in curvature indicate local defects, such as blobs, cracks, surface roughness, and faded embossing and engraving. The results are grouped into local defect regions that are displayed. We demonstrate the algorithm on metal printing and on PLA 3D printing. |
| Author | Kim, Ku-Jin Sacks, Elisha P. Ye, Byounghun |
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| Keywords | Point-to-mesh distance Shape comparison Surface roughness Additive manufacturing Surface defect Curvature |
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| Title | Global and local defect detection for 3D printout surface based on geometric shape comparison |
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