Separation Method for Installation Eccentricity Error of Workpiece
This work solves the challenge of separating the eccentricity error of a workpiece installation from the first harmonic of radial runout error of the spindle, which has a crucial impact on improving the machining quality of the workpiece. Firstly, a mathematical model for the synthesized elliptical...
Saved in:
Main Authors: | , , , |
---|---|
Format: | Article |
Language: | English |
Published: |
MDPI AG
2025-06-01
|
Series: | Applied Sciences |
Subjects: | |
Online Access: | https://www.mdpi.com/2076-3417/15/12/6788 |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Summary: | This work solves the challenge of separating the eccentricity error of a workpiece installation from the first harmonic of radial runout error of the spindle, which has a crucial impact on improving the machining quality of the workpiece. Firstly, a mathematical model for the synthesized elliptical motion for spindle vibration and eccentricity error is established. Subsequently, a novel separation method combining Particle swarm optimization (PSO) and the least squares method (LSM) is proposed. PSO is applied to determine phase angles, and the least squares method is applied to determine amplitudes, achieving precise error separation. Then, numerical simulations were used to verify the effectiveness and reliability of the proposed method, producing a calculation error of less than 0.07% and high consistency (<i>R</i><sup>2</sup> > 0.97). Finally, experimental tests at different spindle speeds, axial distances, and workpieces confirmed the robustness of the method, with a variation in eccentricity error calculation result of less than 0.6%. The results indicate that the installation eccentricity error of the experimental machine tool is independent of the spindle angular velocity and stems from the misalignment of the chuck. This method provides a reliable solution for accurately separating installation eccentricity errors in precision manufacturing. |
---|---|
ISSN: | 2076-3417 |