Airfoil profile reconstruction under the uncertainty of inspection data points
A manufactured aero-engine blade is commonly inspected in sections, and its geometric errors are evaluated from the sectional inspection data points. To maintain consistency in evaluating the geometric errors, in particular, the position and twist errors of the stacked blade sections, reconstruction...
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Published in | International journal of advanced manufacturing technology Vol. 71; no. 1-4; pp. 675 - 683 |
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Main Authors | , |
Format | Journal Article |
Language | English |
Published |
London
Springer London
01.03.2014
Springer Nature B.V |
Subjects | |
Online Access | Get full text |
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Summary: | A manufactured aero-engine blade is commonly inspected in sections, and its geometric errors are evaluated from the sectional inspection data points. To maintain consistency in evaluating the geometric errors, in particular, the position and twist errors of the stacked blade sections, reconstruction of valid sectional airfoil profiles from the measurement points is preferred. Considering that inspection data points are subject to measurement uncertainty, profile reconstruction via approximation-based curve fitting, rather than interpolation-based curve reconstruction, is adopted in this work. The fitting error of the approximated airfoil profile is deemed equivalent to the measurement uncertainty in the inspection data points. Thus, according to a given measurement uncertainty value, a progressive curve fitting scheme is proposed to generate the airfoil profile that meets the measurement uncertainty constraint. A closed nonperiodic B-spline curve is utilized to model the reconstructed airfoil profile due to its versatility in closed curve approximation. Typical computational tests have been carried out to demonstrate the effectiveness of the proposed airfoil profile reconstruction method, which is in fact generic and can be equally applied to approximating other closed sectional profiles. |
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Bibliography: | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 |
ISSN: | 0268-3768 1433-3015 |
DOI: | 10.1007/s00170-013-5527-3 |