Analysis of CMM Dynamic Measurement Error Based on Decision Regression Tree

Abstract

The best way to improve the accuracy of coordinate measuring machine (CMM) is to establish a correlation dynamic error correction model. However, due to the complex source of dynamic measurement error, the traditional method cannot work well. In this paper, decision regression tree is used to analyze the complex relationship between dynamic measurement error and its influencing factors, and the measurement error is predicted. The results show that the algorithm is simple, fast and superior to the least square estimation.