Projects per year
Abstract
Point cloud registration is necessary to join multiple laser scanned data, but researchers have not treated point cloud registration in aircraft assembly in much detail. When applying laser scan metrology into industries, the effects of registration uncertainty on high precision assembly accuracy are not negligible. Based on the use of laser scanner and commercial software PolyWorks, this study investigates the registration uncertainty between part-level and assembly-level point cloud data in aircraft wing assembly. A spar-and-skin assembly with sphere artefacts is used as a case study. Registration uncertainty in gap measurement is also investigated. Results show that: (1) the use of sphere artefacts cannot improve registration accuracy in PolyWorks, but can improve efficiency. (2) Registration process could bring errors and these errors would be affected by the parameters settings during data processing. (3) Systematic errors would be associated with both part-level and assembly-level measurements, and calibration should be applied to eliminate their effects on the measurement of the desired dimensions, i.e. the gap size in this case. It is concluded that laser scanner and computational software can be used for high precision assembly, and evaluating registration uncertainty is a crucial step to improve assembly accuracy.
Original language | English |
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Number of pages | 6 |
Publication status | Published - 22 Jun 2021 |
Event | 2021 IEEE International Workshop on Metrology for AeroSpace - Naples, Italy Duration: 22 Jun 2021 → 25 Jun 2021 |
Conference
Conference | 2021 IEEE International Workshop on Metrology for AeroSpace |
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Country/Territory | Italy |
City | Naples |
Period | 22/06/2021 → 25/06/2021 |
Keywords
- point cloud registration
- laser scanner
- gap measurement
- sphere target
- aircraft wing assembly
Fingerprint
Dive into the research topics of 'Investigation of point cloud registration uncertainty for gap measurement of aircraft wing assembly'. Together they form a unique fingerprint.-
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R1768MEE: Error-propagation Based Geometrical Quality Prediction and Control Strategy (Q-PreMan)
03/04/2017 → 31/12/2020
Project: Research
Prizes
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Best Paper Award - 2nd Classified
Jin, Yan (Recipient), 22 Jun 2021
Prize: Prize (including medals and awards)
Student theses
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Gap volume prediction and uncertainty analysis for aircraft wing assembly
Yang, Y. (Author), Jin, Y. (Supervisor), Abdelal, G. (Supervisor) & Price, M. (Supervisor), Jul 2024Student thesis: Doctoral Thesis › Doctor of Philosophy
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