Andrew Chung Chee Law
Curvature-based segmentation of powder bed point clouds for in-process monitoring
Chung Chee Law, Andrew; Southon, Nicholas; Senin, Nicola; Stavroulakis, Petros; Leach, Richard; Goodridge, Ruth; Kong, Zhenyu
Authors
Nicholas Southon
Nicola Senin
Petros Stavroulakis
Professor RICHARD LEACH RICHARD.LEACH@NOTTINGHAM.AC.UK
CHAIR IN METROLOGY
Professor RUTH GOODRIDGE Ruth.Goodridge@nottingham.ac.uk
PROFESSOR OF ADDITIVE MANUFACTURING
Zhenyu Kong
Abstract
This paper presents a curvature-based analysis of point clouds collected in-process with fringe projection in a polymer powder bed fusion process. The three-dimensional point clouds were obtained from outside of the build chamber with a fringe projection measurement system which was provided with access through an observation window. The curvature-based thresholding of powder bed point clouds demonstrates the ability to separate consolidated areas from the powder bed effectively. This segmentation of the point clouds with masks enables the detection of changes in the outline of consolidated areas between layers, computation of average drop due to the consolidation of the powder bed and separate analysis of both powder bed and consolidated areas. The high-level insights extracted from the analysis of the point clouds could improve process control strategies, such as in-line defect detection during an additive manufacturing build as well as an in-process feedback system for tuning the optimal values of additive process parameters. In summary, we show curvature-based thresholding as an effective segmentation for fringe projection point clouds, which can be further applied to detect defects, such as geometric defects and dimensional inaccuracy.
Citation
Chung Chee Law, A., Southon, N., Senin, N., Stavroulakis, P., Leach, R., Goodridge, R., & Kong, Z. (2018, August). Curvature-based segmentation of powder bed point clouds for in-process monitoring. Presented at 29th Annual International Solid Freeform Fabrication Symposium - An Additive Manufacturing Conference 2018
Presentation Conference Type | Conference Paper (published) |
---|---|
Conference Name | 29th Annual International Solid Freeform Fabrication Symposium - An Additive Manufacturing Conference 2018 |
Start Date | Aug 13, 2018 |
End Date | Aug 15, 2018 |
Acceptance Date | Aug 9, 2018 |
Publication Date | Aug 13, 2018 |
Deposit Date | Oct 25, 2018 |
Publicly Available Date | Oct 25, 2018 |
Book Title | 29th Annual International Solid Freeform Fabrication Symposium - an Additive Manufacturing Conference 2018 |
Chapter Number | n/a |
ISBN | n/a |
Keywords | Additive manufacturing; Polymer powder bed fusion; In-process monitoring; Fringe projection; 3D point cloud processing; Curvature; Segmentation |
Public URL | https://nottingham-repository.worktribe.com/output/1188447 |
Publisher URL | http://sffsymposium.engr.utexas.edu/sites/default/files/2018/016%20CurvatureBasedSegmentationofPowderBedPointC.pdf |
Contract Date | Oct 25, 2018 |
Files
Curvature
(7.8 Mb)
PDF
You might also like
Evaluating approximate and rigorous scattering models in virtual coherence scanning interferometry for improved surface topography measurement
(2024)
Presentation / Conference Contribution
Extracting focus variation data from coherence scanning interferometric measurements
(2024)
Journal Article
Comparison of Fourier optics-based methods for modeling coherence scanning interferometry
(2024)
Journal Article
Vision-based detection and coordinate metrology of a spatially encoded multi-sphere artefact
(2023)
Journal Article
Downloadable Citations
About Repository@Nottingham
Administrator e-mail: discovery-access-systems@nottingham.ac.uk
This application uses the following open-source libraries:
SheetJS Community Edition
Apache License Version 2.0 (http://www.apache.org/licenses/)
PDF.js
Apache License Version 2.0 (http://www.apache.org/licenses/)
Font Awesome
SIL OFL 1.1 (http://scripts.sil.org/OFL)
MIT License (http://opensource.org/licenses/mit-license.html)
CC BY 3.0 ( http://creativecommons.org/licenses/by/3.0/)
Powered by Worktribe © 2025
Advanced Search