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Related Experiment Video

Updated: Aug 23, 2025

Three-Dimensional Finger Motion Tracking during Needling: A Solution for the Kinematic Analysis of Acupuncture Manipulation
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Approximation Model Development and Dynamic Characteristic Analysis Based on Spindle Position of Machining Center.

Ji-Wook Kim1, Dong-Yul Kim2, Hong-In Won1

  • 1Dae-Gyeong Division, Korea Institute of Industrial Technology, Daegu 42994, Korea.

Materials (Basel, Switzerland)
|October 27, 2022
PubMed
Summary

This study evaluated machine tool spindle dynamic characteristics using vibration tests and approximation models. Results show accurate resonant frequency and dynamic stiffness predictions, crucial for optimizing machine tool performance.

Keywords:
approximate modeldesign of experimentsdynamic stiffnessmachining centervibration test

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Area of Science:

  • Mechanical Engineering
  • Vibration Analysis
  • Machine Tool Dynamics

Background:

  • Accurate evaluation of machine tool spindle dynamic characteristics is essential for precision manufacturing.
  • Understanding resonant frequencies and dynamic stiffness across all spindle positions is critical for performance optimization.
  • Existing methods may lack comprehensive accuracy in predicting these dynamic properties.

Purpose of the Study:

  • To evaluate the dynamic characteristics of a machine tool's main spindle at various positions.
  • To develop and validate an approximation model for predicting resonant frequency and dynamic stiffness.
  • To assess the accuracy of different modeling techniques, including kriging and polynomial regression.

Main Methods:

  • A full factorial design was employed to select experimental points for vibration testing.
  • Vibration tests were conducted to measure dynamic characteristics across the spindle.
  • Regression and interpolation methods (kriging, polynomial regression) were used to create and evaluate approximation models.

Main Results:

  • Resonant frequencies were found to be distributed between approximately 236 Hz and 242 Hz.
  • The kriging method achieved approximately 89% accuracy for resonant frequency prediction.
  • Polynomial regression demonstrated the highest accuracy for dynamic stiffness at 81%.
  • Model verification at additional points showed maximum errors of 1.6% for resonant frequency and 7.1% for dynamic stiffness.

Conclusions:

  • The developed approximation models provide reliable predictions of machine tool spindle dynamic characteristics.
  • The kriging and polynomial regression methods offer valuable tools for understanding spindle behavior.
  • The study validates the models' accuracy, supporting their use in machine tool design and analysis.