Machine tool model correction assisted by dynamic evolution sequence

Abstract In the simulation analysis of large-scale industrial instruments such as machine tools, in order to ensure simulation accuracy, model parameter correction is necessary. This research presents a machine tool model correction method assisted by dynamic evolution sequence (DES). The method first introduces a dynamic evolution method to generate a uniformly distributed sequence, replacing the traditional sequence used in Kriging surrogate models, and constructing a more accurate Kriging surrogate model for machine tools. Additionally, incorporating a dynamic evolution sequence instead of a random sequence improves the search space coverage of the Heterogeneous Comprehensive Learning Particle Swarm Optimization (HCLPSO) algorithm. The results of numerical examples demonstrate that the finite element model, corrected using the proposed method, accurately predicts the true displacement responses of the machine tool. This method offers a new solution for addressing large-scale machine tool static model correction problems.