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Sampling plan is an essential part for measuring profile of turbine blade with coordinates measuring machine(CMM), and the detection of feature parameters is a key component of turbine blade inspection. However, the influence of the sampling strategy on the blade feature parameters evaluation has been rarely studied. In order to understand the correlation between the sampling strategy and the accuracy of blade profile feature parameters, firstly, the extraction methods of turbine blade feature parameters were proposed, and these methods were compiled into an executable program TBGeoInspect. Secondly, based on the level principle, the unified mathematical representations of uniform sampling, curvature-based sampling, chord deviation sampling, weighted curvature-based sampling, and curvature-arc length sampling were given. The effect of these five sampling algorithms on the accuracy of turbine blade profile feature parameters was gained through simulation and experiment results of blade sampling. The results validate that the uncertainty of turbine blade feature parameters and sectional curve fitting error are lowest with curvature-arc length sampling method under the same number of sampling points.

Research on Influence of CMM Sampling Points on Detection of Feature Parameters for Turbine Blade