A LS-SVM method for CMM geometric error identification based on spatially integrated measurement

The measurement and identification of geometric errors are important for improving the accuracy of CMM (Coordinate Measuring Machine). Since measurement instruments cannot simultaneously identify all geometric errors, multiple installations for measurements will lead to repeated installation errors between systems, low measurement efficiency, and low identification accuracy. To obtain geometric error of CMM accuractly and conveniently, this paper proposes a method for CMM geometric error identification based on spatially integrated measurement. Specifically, the identification model is determined through the transformation of the spatial comprehensive error model. Then, the LS-SVM(least squares support vector machine) algorithm is adopted for identifying geometric errors. The validity of the proposed method is verified through simulation and experiments. Compared with the ridge regression and least squares methods, the accuracy of the proposed identification method is improved by 35.53% and 46.53%, respectively. The applicability of the proposed method is also verified by setting up another set of experiments.