A Recognition Methodology for the Key Geometric Errors of a Multi-Axis Machine Tool Based on Accuracy Retentivity Analysis
This paper proposes a recognition methodology for key geometric errors using the feature extraction method and accuracy retentivity analysis and presents the approach of optimization compensation of the geometric error of a multiaxis machine tool. The universal kinematics relations of the multiaxis...
Saved in:
| Main Authors: | , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Wiley
2019-01-01
|
| Series: | Complexity |
| Online Access: | http://dx.doi.org/10.1155/2019/8649496 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1832555868451766272 |
|---|---|
| author | Shijie Guo Shufeng Tang Dongsheng Zhang |
| author_facet | Shijie Guo Shufeng Tang Dongsheng Zhang |
| author_sort | Shijie Guo |
| collection | DOAJ |
| description | This paper proposes a recognition methodology for key geometric errors using the feature extraction method and accuracy retentivity analysis and presents the approach of optimization compensation of the geometric error of a multiaxis machine tool. The universal kinematics relations of the multiaxis machine tool are first modelled mathematically based on screw theory. Then, the retentivity of geometric accuracy with respect to the geometric error is defined based on the mapping between the constitutive geometric errors and the time domain. The results show that the variation in the spatial error vector is nonlinear while considering the operation time of the machine tool and the position of the motion axes. Based on this aspect, key factors are extracted that simultaneously consider the correlation, similarity, and sensitivity of the geometric error terms, and the results reveal that the effect of the position-independent geometric errors (PIGEs) on the error vectors of the position and orientation is greater than that of the position-dependent geometric errors (PDGEs) of the linear and rotary axes. Then, the fruit fly optimization algorithm (FOA) is adopted to determine the compensation values through multiobjective tradeoffs between accuracy retentivity and fluctuation in the geometric errors. Finally, an experiment on a four-axis horizontal boring machine tool is used to validate the effectiveness of the proposed approach. The experimental results show that the variations in the precision of each test piece are lower than 25.0%, and the maximum variance in the detection indexes between the finished test pieces is 0.002 mm when the optimized parameters are used for error compensation. This method not only recognizes the key geometric errors but also compensates for the geometric error of the machine tool based on the accuracy retentivity analysis results. The results show that the proposed methodology can effectively enhance the machining accuracy. |
| format | Article |
| id | doaj-art-857d557d2698433a986dd07a2fac61cc |
| institution | Kabale University |
| issn | 1076-2787 1099-0526 |
| language | English |
| publishDate | 2019-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Complexity |
| spelling | doaj-art-857d557d2698433a986dd07a2fac61cc2025-02-03T05:46:57ZengWileyComplexity1076-27871099-05262019-01-01201910.1155/2019/86494968649496A Recognition Methodology for the Key Geometric Errors of a Multi-Axis Machine Tool Based on Accuracy Retentivity AnalysisShijie Guo0Shufeng Tang1Dongsheng Zhang2The College of Mechanical Engineering, Inner Mongolia University of Technology, Hohhot 010051, ChinaThe College of Mechanical Engineering, Inner Mongolia University of Technology, Hohhot 010051, ChinaSchool of Mechanical Engineering, State Key Laboratory for Manufacturing Systems Engineering, Xi’an Jiaotong University, Xi’an 710049, ChinaThis paper proposes a recognition methodology for key geometric errors using the feature extraction method and accuracy retentivity analysis and presents the approach of optimization compensation of the geometric error of a multiaxis machine tool. The universal kinematics relations of the multiaxis machine tool are first modelled mathematically based on screw theory. Then, the retentivity of geometric accuracy with respect to the geometric error is defined based on the mapping between the constitutive geometric errors and the time domain. The results show that the variation in the spatial error vector is nonlinear while considering the operation time of the machine tool and the position of the motion axes. Based on this aspect, key factors are extracted that simultaneously consider the correlation, similarity, and sensitivity of the geometric error terms, and the results reveal that the effect of the position-independent geometric errors (PIGEs) on the error vectors of the position and orientation is greater than that of the position-dependent geometric errors (PDGEs) of the linear and rotary axes. Then, the fruit fly optimization algorithm (FOA) is adopted to determine the compensation values through multiobjective tradeoffs between accuracy retentivity and fluctuation in the geometric errors. Finally, an experiment on a four-axis horizontal boring machine tool is used to validate the effectiveness of the proposed approach. The experimental results show that the variations in the precision of each test piece are lower than 25.0%, and the maximum variance in the detection indexes between the finished test pieces is 0.002 mm when the optimized parameters are used for error compensation. This method not only recognizes the key geometric errors but also compensates for the geometric error of the machine tool based on the accuracy retentivity analysis results. The results show that the proposed methodology can effectively enhance the machining accuracy.http://dx.doi.org/10.1155/2019/8649496 |
| spellingShingle | Shijie Guo Shufeng Tang Dongsheng Zhang A Recognition Methodology for the Key Geometric Errors of a Multi-Axis Machine Tool Based on Accuracy Retentivity Analysis Complexity |
| title | A Recognition Methodology for the Key Geometric Errors of a Multi-Axis Machine Tool Based on Accuracy Retentivity Analysis |
| title_full | A Recognition Methodology for the Key Geometric Errors of a Multi-Axis Machine Tool Based on Accuracy Retentivity Analysis |
| title_fullStr | A Recognition Methodology for the Key Geometric Errors of a Multi-Axis Machine Tool Based on Accuracy Retentivity Analysis |
| title_full_unstemmed | A Recognition Methodology for the Key Geometric Errors of a Multi-Axis Machine Tool Based on Accuracy Retentivity Analysis |
| title_short | A Recognition Methodology for the Key Geometric Errors of a Multi-Axis Machine Tool Based on Accuracy Retentivity Analysis |
| title_sort | recognition methodology for the key geometric errors of a multi axis machine tool based on accuracy retentivity analysis |
| url | http://dx.doi.org/10.1155/2019/8649496 |
| work_keys_str_mv | AT shijieguo arecognitionmethodologyforthekeygeometricerrorsofamultiaxismachinetoolbasedonaccuracyretentivityanalysis AT shufengtang arecognitionmethodologyforthekeygeometricerrorsofamultiaxismachinetoolbasedonaccuracyretentivityanalysis AT dongshengzhang arecognitionmethodologyforthekeygeometricerrorsofamultiaxismachinetoolbasedonaccuracyretentivityanalysis AT shijieguo recognitionmethodologyforthekeygeometricerrorsofamultiaxismachinetoolbasedonaccuracyretentivityanalysis AT shufengtang recognitionmethodologyforthekeygeometricerrorsofamultiaxismachinetoolbasedonaccuracyretentivityanalysis AT dongshengzhang recognitionmethodologyforthekeygeometricerrorsofamultiaxismachinetoolbasedonaccuracyretentivityanalysis |