Machine tool FEM 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 the dynamic evolution sequence (DES). The method...
Saved in:
| Main Authors: | , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Nature Portfolio
2025-05-01
|
| Series: | Scientific Reports |
| Subjects: | |
| Online Access: | https://doi.org/10.1038/s41598-025-03058-9 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1850231447349100544 |
|---|---|
| author | Weihao Lin Peng Zhong Xindi Wei Li Zhu Xuanlong Wu |
| author_facet | Weihao Lin Peng Zhong Xindi Wei Li Zhu Xuanlong Wu |
| author_sort | Weihao Lin |
| collection | DOAJ |
| description | 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 the 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. Moreover, replacing the random sequence with a dynamic evolution sequence enhances 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. |
| format | Article |
| id | doaj-art-e58fc707b49f42ea95e9b3d04fba2603 |
| institution | OA Journals |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-e58fc707b49f42ea95e9b3d04fba26032025-08-20T02:03:31ZengNature PortfolioScientific Reports2045-23222025-05-0115111410.1038/s41598-025-03058-9Machine tool FEM model correction assisted by dynamic evolution sequenceWeihao Lin0Peng Zhong1Xindi Wei2Li Zhu3Xuanlong Wu4State Key Laboratory of Structural Analysis, Optimization and CAE Software for Industrial Equipment, School of Mechanics and Aerospace Engineering, Dalian University of TechnologyState Key Laboratory of Structural Analysis, Optimization and CAE Software for Industrial Equipment, School of Mechanics and Aerospace Engineering, Dalian University of TechnologyState Key Laboratory of Structural Analysis, Optimization and CAE Software for Industrial Equipment, School of Mechanics and Aerospace Engineering, Dalian University of TechnologyState Key Laboratory of Structural Analysis, Optimization and CAE Software for Industrial Equipment, School of Mechanics and Aerospace Engineering, Dalian University of TechnologyState Key Laboratory of Structural Analysis, Optimization and CAE Software for Industrial Equipment, School of Mechanics and Aerospace Engineering, Dalian University of TechnologyAbstract 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 the 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. Moreover, replacing the random sequence with a dynamic evolution sequence enhances 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.https://doi.org/10.1038/s41598-025-03058-9Machine tool FEM modelModel correctionKriging surrogate modelsHeterogeneous comprehensive learning particle swarm optimizationDynamic evolutionLow-discrepancy sequence |
| spellingShingle | Weihao Lin Peng Zhong Xindi Wei Li Zhu Xuanlong Wu Machine tool FEM model correction assisted by dynamic evolution sequence Scientific Reports Machine tool FEM model Model correction Kriging surrogate models Heterogeneous comprehensive learning particle swarm optimization Dynamic evolution Low-discrepancy sequence |
| title | Machine tool FEM model correction assisted by dynamic evolution sequence |
| title_full | Machine tool FEM model correction assisted by dynamic evolution sequence |
| title_fullStr | Machine tool FEM model correction assisted by dynamic evolution sequence |
| title_full_unstemmed | Machine tool FEM model correction assisted by dynamic evolution sequence |
| title_short | Machine tool FEM model correction assisted by dynamic evolution sequence |
| title_sort | machine tool fem model correction assisted by dynamic evolution sequence |
| topic | Machine tool FEM model Model correction Kriging surrogate models Heterogeneous comprehensive learning particle swarm optimization Dynamic evolution Low-discrepancy sequence |
| url | https://doi.org/10.1038/s41598-025-03058-9 |
| work_keys_str_mv | AT weihaolin machinetoolfemmodelcorrectionassistedbydynamicevolutionsequence AT pengzhong machinetoolfemmodelcorrectionassistedbydynamicevolutionsequence AT xindiwei machinetoolfemmodelcorrectionassistedbydynamicevolutionsequence AT lizhu machinetoolfemmodelcorrectionassistedbydynamicevolutionsequence AT xuanlongwu machinetoolfemmodelcorrectionassistedbydynamicevolutionsequence |