Multiphysics simulation of a precision manufacturing machine with experimental validation and prediction.
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| Title: | Multiphysics simulation of a precision manufacturing machine with experimental validation and prediction. |
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| Authors: | Behera, Ratnakar1 (AUTHOR) ratanbbsrk@gmail.com, Chan, Tzu-Chi2 (AUTHOR) tcchan@nfu.edu.tw |
| Source: | Sādhanā: Academy Proceedings in Engineering Sciences. Jun2026, Vol. 51 Issue 2, p1-20. 20p. |
| Subjects: | Modal analysis, Artificial neural networks, Dynamic stability, Machining, Mechanical vibration research, Finite element method, Computer simulation |
| Abstract: | This study investigates the dynamic and structural behavior of a precision milling machine using finite-element analysis (FEA) and experimental modal analysis (EMA), and further predicts results using artificial neural networks (ANNs). Modal analysis is performed to identify natural frequencies and mode shapes, thereby highlighting potential resonance issues. The natural frequencies predicted by FEA are 79.6, 86.7, 114, 128.9, and 166.6 Hz, while experimental modal testing yields corresponding values of 83.8, 90.7, 104, 120, and 166 Hz, with percentage errors ranging from −5 to 9.6%. Static structural analysis is conducted to assess mechanical stability under operational loading conditions, revealing a maximum total deformation of 0.12 mm and directional deformations of 0.02 mm along the X-axis, 0.11 mm along the Y-axis, and 0.05 mm along the Z-axis. Harmonic analysis is employed to evaluate the vibrational response of the structure under periodic excitation forces. Experimental validation is performed using impact hammer testing, and the results are further assessed using the modal assurance criterion (MAC) and the coordinate modal assurance criterion (Co-MAC) to ensure accuracy. ANN models trained using both FEA and experimental data demonstrate high predictive capability, achieving correlation coefficients (R-values) of 0.944 for training, 0.925 for testing, and 0.947 for validation. The ANN effectively captures complex relationships and hidden patterns in the data that are not directly observable through conventional FEA. Overall, the findings enhance predictive accuracy and computational efficiency, thereby improving machine stability, reducing vibration levels, and optimizing precision machining performance. [ABSTRACT FROM AUTHOR] |
| Copyright of Sādhanā: Academy Proceedings in Engineering Sciences is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Engineering Source |
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| Abstract: | This study investigates the dynamic and structural behavior of a precision milling machine using finite-element analysis (FEA) and experimental modal analysis (EMA), and further predicts results using artificial neural networks (ANNs). Modal analysis is performed to identify natural frequencies and mode shapes, thereby highlighting potential resonance issues. The natural frequencies predicted by FEA are 79.6, 86.7, 114, 128.9, and 166.6 Hz, while experimental modal testing yields corresponding values of 83.8, 90.7, 104, 120, and 166 Hz, with percentage errors ranging from −5 to 9.6%. Static structural analysis is conducted to assess mechanical stability under operational loading conditions, revealing a maximum total deformation of 0.12 mm and directional deformations of 0.02 mm along the X-axis, 0.11 mm along the Y-axis, and 0.05 mm along the Z-axis. Harmonic analysis is employed to evaluate the vibrational response of the structure under periodic excitation forces. Experimental validation is performed using impact hammer testing, and the results are further assessed using the modal assurance criterion (MAC) and the coordinate modal assurance criterion (Co-MAC) to ensure accuracy. ANN models trained using both FEA and experimental data demonstrate high predictive capability, achieving correlation coefficients (R-values) of 0.944 for training, 0.925 for testing, and 0.947 for validation. The ANN effectively captures complex relationships and hidden patterns in the data that are not directly observable through conventional FEA. Overall, the findings enhance predictive accuracy and computational efficiency, thereby improving machine stability, reducing vibration levels, and optimizing precision machining performance. [ABSTRACT FROM AUTHOR] |
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| ISSN: | 02562499 |
| DOI: | 10.1007/s12046-026-03109-5 |
