Modal parameters prediction for robotic milling based on Gaussian process regression
The acquisition of the frequency response function of the robotic structure and the identification of dynam ic parameters have a significant impact on the prediction of robotic milling，and modal parameters have strong posture-dependence. The finite element method and dynamic model often lose accurac...
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| Format: | Article |
| Language: | zho |
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Editorial Department of Advances in Aeronautical Science and Engineering
2024-12-01
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| Series: | Hangkong gongcheng jinzhan |
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| Online Access: | http://hkgcjz.cnjournals.com/hkgcjz/article/abstract/2024086 |
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| _version_ | 1846091447055417344 |
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| author | WAN Min LI Zhanying SHEN Chuanjing WU Xiaojie |
| author_facet | WAN Min LI Zhanying SHEN Chuanjing WU Xiaojie |
| author_sort | WAN Min |
| collection | DOAJ |
| description | The acquisition of the frequency response function of the robotic structure and the identification of dynam ic parameters have a significant impact on the prediction of robotic milling，and modal parameters have strong posture-dependence. The finite element method and dynamic model often lose accuracy due to the difficulty in exactly modeling the stiffness and damping properties of robots. To predict the modal parameters quickly and accurately in all robot postures within the machining space，this paper proposes a modal parameter prediction method based on Gaussian process regression. The influence of joint angles and Euler angles of a six degree-of-freedom serial robot on the modal parameters of the robotic milling system is investigated. Based on this，a posture-related modal parameters prediction model is established to characterize the relationship between modal parameters and robot postures through 245 sets of modal percussion experiments in the machining plane. The model can predict the posturerelated modal parameters for all robot postures by a limited number of modal testing experiments. Results show that the proposed method is validated by experiments. |
| format | Article |
| id | doaj-art-8825779b493f4bebbf93ae4deaafb9d6 |
| institution | Kabale University |
| issn | 1674-8190 |
| language | zho |
| publishDate | 2024-12-01 |
| publisher | Editorial Department of Advances in Aeronautical Science and Engineering |
| record_format | Article |
| series | Hangkong gongcheng jinzhan |
| spelling | doaj-art-8825779b493f4bebbf93ae4deaafb9d62025-01-10T06:25:15ZzhoEditorial Department of Advances in Aeronautical Science and EngineeringHangkong gongcheng jinzhan1674-81902024-12-0115617418810.16615/j.cnki.1674-8190.2024.06.1610.16615/j.cnki.1674-8190.2024.06.16Modal parameters prediction for robotic milling based on Gaussian process regressionWAN Min0LI Zhanying1SHEN Chuanjing2WU Xiaojie3School of Mechanical Engineering, Northwestern Polytechnical University, Xi'an 710072, ChinaSchool of Mechanical Engineering, Northwestern Polytechnical University, Xi'an 710072, ChinaSchool of Mechanical Engineering, Northwestern Polytechnical University, Xi'an 710072, ChinaAVIC Xinxiang Aviation Industry(Group) Co., Ltd., Xinxiang 453049, ChinaThe acquisition of the frequency response function of the robotic structure and the identification of dynam ic parameters have a significant impact on the prediction of robotic milling，and modal parameters have strong posture-dependence. The finite element method and dynamic model often lose accuracy due to the difficulty in exactly modeling the stiffness and damping properties of robots. To predict the modal parameters quickly and accurately in all robot postures within the machining space，this paper proposes a modal parameter prediction method based on Gaussian process regression. The influence of joint angles and Euler angles of a six degree-of-freedom serial robot on the modal parameters of the robotic milling system is investigated. Based on this，a posture-related modal parameters prediction model is established to characterize the relationship between modal parameters and robot postures through 245 sets of modal percussion experiments in the machining plane. The model can predict the posturerelated modal parameters for all robot postures by a limited number of modal testing experiments. Results show that the proposed method is validated by experiments.http://hkgcjz.cnjournals.com/hkgcjz/article/abstract/2024086robotic millingposture-dependencefrequency response functions （frfs）modal parametersgaussian process regression |
| spellingShingle | WAN Min LI Zhanying SHEN Chuanjing WU Xiaojie Modal parameters prediction for robotic milling based on Gaussian process regression Hangkong gongcheng jinzhan robotic milling posture-dependence frequency response functions （frfs） modal parameters gaussian process regression |
| title | Modal parameters prediction for robotic milling based on Gaussian process regression |
| title_full | Modal parameters prediction for robotic milling based on Gaussian process regression |
| title_fullStr | Modal parameters prediction for robotic milling based on Gaussian process regression |
| title_full_unstemmed | Modal parameters prediction for robotic milling based on Gaussian process regression |
| title_short | Modal parameters prediction for robotic milling based on Gaussian process regression |
| title_sort | modal parameters prediction for robotic milling based on gaussian process regression |
| topic | robotic milling posture-dependence frequency response functions （frfs） modal parameters gaussian process regression |
| url | http://hkgcjz.cnjournals.com/hkgcjz/article/abstract/2024086 |
| work_keys_str_mv | AT wanmin modalparameterspredictionforroboticmillingbasedongaussianprocessregression AT lizhanying modalparameterspredictionforroboticmillingbasedongaussianprocessregression AT shenchuanjing modalparameterspredictionforroboticmillingbasedongaussianprocessregression AT wuxiaojie modalparameterspredictionforroboticmillingbasedongaussianprocessregression |