Research on blade statistics measurement registration planning technology

Abstract

Blade has the characteristics of high machining quality of complex curved surface. If there is no benchmark before machining, it is impossible to judge whether the blades before machining are qualified. Therefore, it is necessary to analyze the blade measurement data. Due to the large measurement point error and disordered distribution, it is necessary to optimize the blade registration. Therefore, blade model registration and positioning is particularly important in blade shape detection and analysis. First, preregistration is carried out based on the six point optimization selection of the blade. After preregistration, the selection method of registration control point set based on theoretical model and statistical error is proposed, planning the registration datum point set on the blade model. The registration control point set is obtained through the registration operation between the measurement data and the registration reference point set. Finally, based on the stability and reliability of important sampling sensitivity and statistical error, obtain the probability density function of error normal distribution statistics samples and important samples. The selection of statistical control points and the rationality of the objective function were verified. The stability/reliability of the statistical alignment point selection is proved to be feasible. The statistical registration deviation is [0.015,0.026] mm, and the ICP registration deviation is [0.031,0.035] mm. The average deviation of statistics registration is about 0.013 mm smaller than the average deviation of ICP registration. The deviation of statistical sampling points is about 0.0214 mm, and that of traditional sampling points is about 0.0275 mm. The deviation of statistical sampling points is about 0.0061 mm smaller than that of traditional sampling points. It meets the requirements of rapid, high efficiency and high precision measurement for aeroengine blades.

Keywords

Shape detection pre-registration registration control point set sensitivity probability density function

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