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

CMM采样点对涡轮叶片特征参数检测精度的影响

School of Mechanical Engineering, Northwestern Polytechnical University, Xi’an 710072, China

Received: 30 September 2018

Abstract

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.

摘要

采样点规划是涡轮叶片三坐标测量的必要步骤，特征参数是涡轮叶片的重要检测内容之一。但目前，采样点分布对涡轮叶片特征参数检测精度的影响研究尚处于空白。针对此问题，给出了涡轮叶片特征参数的计算方法并开发了相应软件；基于杠杆平衡原理，研究了适用于自由曲线曲面的均匀采样、曲率采样、弦公差采样、加权曲率采样和曲率-弧长采样算法。通过测量仿真与实验，研究了这5种采样算法对涡轮叶片叶型特征参数计算精度的影响；结果表明：采用曲率-弧长采样算法得到的叶型特征参数的计算误差和叶型曲线拟合误差均小于其他4种采样算法。