Thermal Error Modeling of Numerical Control Machine Based on Beetle Antennae Search Back-propagation Neural Networks
Abstract Thermal errors are one key impact factor on the processing accuracy of numerical control machine. This study targeted at a certain vertical processing center presents a new algorithm for predictive modeling of thermal errors in numerical control machine. This algorithm is founded on back-pr...
| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Springer
2023-05-01
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| Series: | International Journal of Computational Intelligence Systems |
| Subjects: | |
| Online Access: | https://doi.org/10.1007/s44196-023-00263-0 |
| _version_ | 1797822658829615104 |
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| author | Li Bao Yulong Xu Qiang Zhou Peng Gao Xiaoxia Guo Ziqi Liu Hui Jiang |
| author_facet | Li Bao Yulong Xu Qiang Zhou Peng Gao Xiaoxia Guo Ziqi Liu Hui Jiang |
| author_sort | Li Bao |
| collection | DOAJ |
| description | Abstract Thermal errors are one key impact factor on the processing accuracy of numerical control machine. This study targeted at a certain vertical processing center presents a new algorithm for predictive modeling of thermal errors in numerical control machine. This algorithm is founded on back-propagation neural networks (BPNNs) and adopts beetle antennae search (BAS) to find the best weights and thresholds of BPNNs. It avoids the local minimization due to local extremums faced by traditional BPNNs. The intermingling rate and arithmetic computation efficiency of neural network algorithms are further improved. Then, a BAS-BP thermal error prediction model is built with the machine temperature changes and thermal errors as the input data. Compared with conventional BPNNs, the BPNN after particle swarm optimization suggests the convergence rate of BAS-BP is improved by 85%, the leftover mistakes between the genuine information and the anticipated information are under 1 um, and the overall prediction precision is above 90%. Thus, the new model has high precision, high anti-disturbance ability and strong robustness. |
| first_indexed | 2024-03-13T10:12:23Z |
| format | Article |
| id | doaj.art-2ba24b0feffd4e649f89991ef561f551 |
| institution | Directory Open Access Journal |
| issn | 1875-6883 |
| language | English |
| last_indexed | 2024-03-13T10:12:23Z |
| publishDate | 2023-05-01 |
| publisher | Springer |
| record_format | Article |
| series | International Journal of Computational Intelligence Systems |
| spelling | doaj.art-2ba24b0feffd4e649f89991ef561f5512023-05-21T11:26:54ZengSpringerInternational Journal of Computational Intelligence Systems1875-68832023-05-0116111610.1007/s44196-023-00263-0Thermal Error Modeling of Numerical Control Machine Based on Beetle Antennae Search Back-propagation Neural NetworksLi Bao0Yulong Xu1Qiang Zhou2Peng Gao3Xiaoxia Guo4Ziqi Liu5Hui Jiang6School of Mechanical and Electrical Engineering, Qiqihar UniversitySchool of Mechanical and Electrical Engineering, Qiqihar UniversitySchool of Mechanical and Electrical Engineering, Qiqihar UniversitySchool of Mechanical and Electrical Engineering, Qiqihar UniversitySchool of Mechanical and Electrical Engineering, Qiqihar UniversitySchool of Mechanical and Electrical Engineering, Qiqihar UniversityQiqihar Heavy CNC Equipment Corp., LtdAbstract Thermal errors are one key impact factor on the processing accuracy of numerical control machine. This study targeted at a certain vertical processing center presents a new algorithm for predictive modeling of thermal errors in numerical control machine. This algorithm is founded on back-propagation neural networks (BPNNs) and adopts beetle antennae search (BAS) to find the best weights and thresholds of BPNNs. It avoids the local minimization due to local extremums faced by traditional BPNNs. The intermingling rate and arithmetic computation efficiency of neural network algorithms are further improved. Then, a BAS-BP thermal error prediction model is built with the machine temperature changes and thermal errors as the input data. Compared with conventional BPNNs, the BPNN after particle swarm optimization suggests the convergence rate of BAS-BP is improved by 85%, the leftover mistakes between the genuine information and the anticipated information are under 1 um, and the overall prediction precision is above 90%. Thus, the new model has high precision, high anti-disturbance ability and strong robustness.https://doi.org/10.1007/s44196-023-00263-0Thermal error modelingError compensationIntelligent algorithmBack-propagation neural networkBeetle antennae search algorithm |
| spellingShingle | Li Bao Yulong Xu Qiang Zhou Peng Gao Xiaoxia Guo Ziqi Liu Hui Jiang Thermal Error Modeling of Numerical Control Machine Based on Beetle Antennae Search Back-propagation Neural Networks International Journal of Computational Intelligence Systems Thermal error modeling Error compensation Intelligent algorithm Back-propagation neural network Beetle antennae search algorithm |
| title | Thermal Error Modeling of Numerical Control Machine Based on Beetle Antennae Search Back-propagation Neural Networks |
| title_full | Thermal Error Modeling of Numerical Control Machine Based on Beetle Antennae Search Back-propagation Neural Networks |
| title_fullStr | Thermal Error Modeling of Numerical Control Machine Based on Beetle Antennae Search Back-propagation Neural Networks |
| title_full_unstemmed | Thermal Error Modeling of Numerical Control Machine Based on Beetle Antennae Search Back-propagation Neural Networks |
| title_short | Thermal Error Modeling of Numerical Control Machine Based on Beetle Antennae Search Back-propagation Neural Networks |
| title_sort | thermal error modeling of numerical control machine based on beetle antennae search back propagation neural networks |
| topic | Thermal error modeling Error compensation Intelligent algorithm Back-propagation neural network Beetle antennae search algorithm |
| url | https://doi.org/10.1007/s44196-023-00263-0 |
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