<  Back to the Polytechnique Montréal portal

Scan-to-CAD alignment of damaged airfoil blade point clouds through geometric dissimilarity assessment

Hamid Ghorbani and Farbod Khameneifar

Paper (2021)

Open Acess document at official publisher
An external link is available for this item
Show abstract
Hide abstract

Abstract

This paper presents a method for accurate alignment of the scanned point clouds of damaged blades with their nominal CAD model, which is an essential task in automated inspection for remanufacturing. The geometric dissimilarity of the underlying surface of the local neighborhoods of each measured data point and its nearest corresponding point on the CAD model is evaluated using a metric combining the average curvature Hausdorff distance and average Euclidean Hausdorff distance. The algorithm eliminates unreliable pairs with high geometric dissimilarity values in damaged regions from the matching process. The effectiveness of the proposed method is verified by experimental tests.

Uncontrolled Keywords

Damaged blade inspection; Scan-to-CAD Registration; Averaging-out error; Geometric dissimilarity

Department: Department of Mechanical Engineering
PolyPublie URL: https://publications.polymtl.ca/50651/
Conference Title: 15th CIRP Conference on Intelligent Computation in Manufacturing Engineering (CIRP ICME 2021)
Conference Location: Gulf on Naples, Italy
Conference Date(s): 2021-07-14 - 2021-07-16
Editors: Roberto Teti and Doriana D'addona
Journal Title: Procedia CIRP (vol. 112)
Publisher: Elsevier B.V.
DOI: 10.1016/j.procir.2022.09.060
Official URL: https://doi.org/10.1016/j.procir.2022.09.060
Date Deposited: 18 Apr 2023 14:59
Last Modified: 05 Apr 2024 11:54
Cite in APA 7: Ghorbani, H., & Khameneifar, F. (2021, July). Scan-to-CAD alignment of damaged airfoil blade point clouds through geometric dissimilarity assessment [Paper]. 15th CIRP Conference on Intelligent Computation in Manufacturing Engineering (CIRP ICME 2021), Gulf on Naples, Italy (5 pages). Published in Procedia CIRP, 112. https://doi.org/10.1016/j.procir.2022.09.060

Statistics

Dimensions

Repository Staff Only

View Item View Item