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Peer-reviewedCC BY 4.0PLOS One

Parametric finite element modeling and pose-dependent natural characteristics of a five-axis machine tool

  1. 1School of Mechanical Engineering, Yangzhou Polytechnic College, Yangzhou, China
  2. 2Beijing Institute of Fashion Technology, Beijing, China
  3. 3School of Mechanical Science and Engineering, Huazhong University of Science and Technology, Wuhan, China

Abstract

Five-axis machine tools exhibit pose-dependent structural dynamics, but repeated full finite element reconstruction across the workspace is computationally inefficient. We developed a parametric finite element workflow in ANSYS APDL for a five-axis machine-tool analog specimen. The structure was divided into six modules; regular regions were sweep-meshed and irregular regions were represented with quadratic SOLID186 elements using nominal element sizes of 20, 10, and 5 mm. Bearing, ball screw-nut, and linear guideway-slider joints were represented by equivalent springs. Twelve screw-nut and guideway-slider stiffness parameters were calibrated with a genetic algorithm against the first six natural frequencies measured at one reference pose. Movable joint locations were updated from the X-, Y-, Z-, A-, and C-axis pose variables while the identified joint stiffness values were held constant. At the reference pose, the mean and maximum frequency discrepancies between the calibrated model and experiment were 3.29% and 8.78%, respectively. Workspace calculations predicted that the first natural frequency decreased as the beam center of gravity rose, whereas spindle-dominated local modes showed similar trends among working planes. Because calibration and model checking used the same reference-pose data and no multi-pose modal measurements were available, the reported workspace trends should be interpreted as model-based predictions rather than independently validated responses.

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BibTeX
@article{zhang2026parametric,
  title = {{Parametric finite element modeling and pose-dependent natural characteristics of a five-axis machine tool}},
  author = {Jiamin Zhang and Jiahui Zhang and Ning Lü and Shuaiqi Chen},
  journal = {PLOS One},
  year = {2026},
  month = oct,
  doi = {10.1371/journal.pone.0359727},
  url = {https://doi.org/10.1371/journal.pone.0359727},
  note = {CC BY 4.0},
}
RIS
TY  - JOUR
TI  - Parametric finite element modeling and pose-dependent natural characteristics of a five-axis machine tool
AU  - Jiamin Zhang
AU  - Jiahui Zhang
AU  - Ning Lü
AU  - Shuaiqi Chen
PY  - 2026
DA  - 2026/10/05
JO  - PLOS One
DO  - 10.1371/journal.pone.0359727
UR  - https://doi.org/10.1371/journal.pone.0359727
AB  - Five-axis machine tools exhibit pose-dependent structural dynamics, but repeated full finite element reconstruction across the workspace is computationally inefficient. We developed a parametric finite element workflow in ANSYS APDL for a five-axis machine-tool analog specimen. The structure was divided into six modules; regular regions were sweep-meshed and irregular regions were represented with quadratic SOLID186 elements using nominal element sizes of 20, 10, and 5 mm. Bearing, ball screw-nut, and linear guideway-slider joints were represented by equivalent springs. Twelve screw-nut and guideway-slider stiffness parameters were calibrated with a genetic algorithm against the first six natural frequencies measured at one reference pose. Movable joint locations were updated from the X-, Y-, Z-, A-, and C-axis pose variables while the identified joint stiffness values were held constant. At the reference pose, the mean and maximum frequency discrepancies between the calibrated model and experiment were 3.29% and 8.78%, respectively. Workspace calculations predicted that the first natural frequency decreased as the beam center of gravity rose, whereas spindle-dominated local modes showed similar trends among working planes. Because calibration and model checking used the same reference-pose data and no multi-pose modal measurements were available, the reported workspace trends should be interpreted as model-based predictions rather than independently validated responses.
SN  - 1932-6203
ER  -