Modeling and Prediction Method for CNC Machine Tools’ Errors Based on Spatial Feature Points
In order to improve the precision of CNC machine tools effectively, a method for modeling and predicting their spatial errors based on spatial feature points was proposed. Taking three-axis vertical CNC machine tools as the research object, we think that the whole space formed by machine tools’ work...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2020-01-01
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| Series: | Advances in Materials Science and Engineering |
| Online Access: | http://dx.doi.org/10.1155/2020/7254596 |
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| author | Guohua Chen Lin Zhang Hua Xiang Yong Chen |
| author_facet | Guohua Chen Lin Zhang Hua Xiang Yong Chen |
| author_sort | Guohua Chen |
| collection | DOAJ |
| description | In order to improve the precision of CNC machine tools effectively, a method for modeling and predicting their spatial errors based on spatial feature points was proposed. Taking three-axis vertical CNC machine tools as the research object, we think that the whole space formed by machine tools’ working can be seen as the combination of a number of cubes, whose vertices are considered to be feature points, and others in the cubes are called nonfeature points. So, each nonfeature point’s errors can be predicted by the cube’s eight vertices’ errors. Based on the above ideas, an approach including the installing instrument for measuring any spatial feature point’s errors was put forward. In this way, all data of the feature points’ errors could be obtained. Moreover, according to these error data, the prediction model of nonfeature points’ errors was established by using the internal division ratio method. The method has the advantages of small interpolation operation, easy integration in the numerical control system, and high compensation precision. Finally, an example was used to prove its effectiveness and feasibility. |
| format | Article |
| id | doaj-art-44eccc83400948029acc10a90863b03e |
| institution | Kabale University |
| issn | 1687-8434 1687-8442 |
| language | English |
| publishDate | 2020-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Advances in Materials Science and Engineering |
| spelling | doaj-art-44eccc83400948029acc10a90863b03e2025-02-03T01:25:49ZengWileyAdvances in Materials Science and Engineering1687-84341687-84422020-01-01202010.1155/2020/72545967254596Modeling and Prediction Method for CNC Machine Tools’ Errors Based on Spatial Feature PointsGuohua Chen0Lin Zhang1Hua Xiang2Yong Chen3School of Mechanical Engineering, Hubei University of Arts and Science, Xiangyang 441053, ChinaSchool of Mechanical Engineering, Hubei University of Arts and Science, Xiangyang 441053, ChinaInstitute of Advanced Manufacturing Engineering of Huazhong University of Science and Technology, Xiangyang 441053, ChinaSchool of Mechanical Science and Engineering, Huazhong University of Science and Technology, Wuhan 430074, ChinaIn order to improve the precision of CNC machine tools effectively, a method for modeling and predicting their spatial errors based on spatial feature points was proposed. Taking three-axis vertical CNC machine tools as the research object, we think that the whole space formed by machine tools’ working can be seen as the combination of a number of cubes, whose vertices are considered to be feature points, and others in the cubes are called nonfeature points. So, each nonfeature point’s errors can be predicted by the cube’s eight vertices’ errors. Based on the above ideas, an approach including the installing instrument for measuring any spatial feature point’s errors was put forward. In this way, all data of the feature points’ errors could be obtained. Moreover, according to these error data, the prediction model of nonfeature points’ errors was established by using the internal division ratio method. The method has the advantages of small interpolation operation, easy integration in the numerical control system, and high compensation precision. Finally, an example was used to prove its effectiveness and feasibility.http://dx.doi.org/10.1155/2020/7254596 |
| spellingShingle | Guohua Chen Lin Zhang Hua Xiang Yong Chen Modeling and Prediction Method for CNC Machine Tools’ Errors Based on Spatial Feature Points Advances in Materials Science and Engineering |
| title | Modeling and Prediction Method for CNC Machine Tools’ Errors Based on Spatial Feature Points |
| title_full | Modeling and Prediction Method for CNC Machine Tools’ Errors Based on Spatial Feature Points |
| title_fullStr | Modeling and Prediction Method for CNC Machine Tools’ Errors Based on Spatial Feature Points |
| title_full_unstemmed | Modeling and Prediction Method for CNC Machine Tools’ Errors Based on Spatial Feature Points |
| title_short | Modeling and Prediction Method for CNC Machine Tools’ Errors Based on Spatial Feature Points |
| title_sort | modeling and prediction method for cnc machine tools errors based on spatial feature points |
| url | http://dx.doi.org/10.1155/2020/7254596 |
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