An adaptive sampling methodology for measuring blades with CMM based on dominant feature points

Abstract

Sampling strategy is one of the most critical components of the contact probing process with coordinate measuring machines (CMMs). The number and location of sampling points have direct impacts on the efficiency and reliability of the measurements. Blades of aviation engines have complex geometry and extremely high precision requirements, but the sampling strategy of blades is still a difficult problem which limits the use of CMMs in blade inspection. To balance the measuring efficiency and accuracy of CMM, an adaptive sampling algorithm based on dominant feature points is proposed. In this algorithm, the points are adaptively sampled through a modified B-spline least-square fitting process with dominant feature points and new dominant points if necessary, and the new dominant points are computed by a new method, namely weighted curvature mass moment method. The sections of the blade to be measured are determined with this adaptive sampling algorithm to be the leading and trailing edge curves, and the points on each section to be measured are determined using dominant feature points of the blade profile. The definition and extraction methods for dominant feature points of the blade profile are also researched. By comparing the reconstruction errors of sampling points for several different blades, this adaptive sampling algorithm is proved to be more efficient and accurate than the equal parameter sampling method, equal arc length sampling method, and chord deviation sampling method.