Automated measurement and hybrid adaptive identification method for kinematic calibration of hybrid machine tools

Abstract

Kinematic calibration is an effective method for ensuring the accuracy of hybrid machine tools. Traditional calibration methods are limited by cumbersome operations and poor algorithm robustness. In this study, measurement methods and identification algorithms are studied, and a new calibration method is proposed. First, the configuration of the hybrid machine tool is described, and an error model is constructed. Then, an automated measurement method of tool center point position error for multiple test arbors is proposed using the R-test device. Subsequently, a hybrid adaptive identification algorithm is proposed by sequentially solving the unit inhomogeneity of the identification model, the rank deficiency produced by incomplete measurements, and the multicollinearity between error parameters. Finally, the efficacy of the proposed calibration method is verified through simulations and experiments. The proposed method significantly improves the calibration efficiency and accuracy and proposes a new solution for automated calibration.