Abstract
As the critical component of aero-engines, the geometric accuracy and surface quality of the blade are essential for ensuring engine manufacturing quality and enhancing overall performance. This article proposes the measurement method for the surface profile of aero-engine blades based on multiline laser sensing, which demonstrates excellent applicability for the 3-D surface measurement. The predetermined relative orientation between the binocular measurement mechanism and the axis of the rotary table is resolved and enhanced through parameter optimization, which significantly simplifies the pre-measurement setup and calibration process. Utilizing the dual constraints of binocular vision and active multilaser projection, feature point matching and positioning of spatial data points are determined, and the complete contour profile of the sample blade is then integrated through the data integration method. Furthermore, the piecewise linear interpolation method is proposed to sort blade cross-sectional data, thereby enabling the extraction of maximum thickness parameters. Utilizing the axis calibration and parameter optimization, the proposed measurement method achieves the average deviation of all the corner points relative to the first measurement of 0.1789 pixels and the standard deviation of 0.0026 pixels across ten repeated calibration experiments. Consequently, multiple evaluations at different cross-sectional heights indicate that the maximum difference between the proposed measurement method and the FaroArm measurement instrument is 0.0063 mm, which aims to validate the measurement efficacy. The proposed method demonstrates high efficiency and accuracy in the 3-D surface metrology of aero-engine blades. Furthermore, we will focus on extending the application to a wider range of aero-engine components.
| Original language | English |
|---|---|
| Article number | 1002910 |
| Journal | IEEE Transactions on Instrumentation and Measurement |
| Volume | 74 |
| DOIs | |
| State | Published - 2025 |
Keywords
- Cross-sectional extraction
- feature point matching
- multiline laser sensing
- parameter optimization
- rotational scanning
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