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A Regularized Regression Thermal Error Modeling Method for CNC Machine Tools under Different Ambient Temperatures and

Xinyuan Wei1, Honghan Ye2, Jinghuan Zhou1

  • 1School of Electrical and Information Engineering, Anhui University of Technology, Ma'anshan 230009, China.

Sensors (Basel, Switzerland)
|July 11, 2023
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Summary

This study introduces a simpler regularized regression model for predicting CNC machine tool thermal errors, outperforming complex deep learning methods in accuracy and robustness.

Keywords:
CNC machine toolsleast absolute regressionpracticabilityregularizationthermal error modeling

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Area of Science:

  • Manufacturing Engineering
  • Metrology
  • Machine Learning

Background:

  • Thermal errors significantly impact CNC machine tool precision.
  • Existing deep learning models for thermal error prediction are complex and data-intensive.
  • Interpretability and practical implementation remain challenges in current methods.

Purpose of the Study:

  • To propose a novel, interpretable, and practical regularized regression algorithm for CNC machine tool thermal error modeling.
  • To enable automatic selection of temperature-sensitive variables for improved model efficiency.
  • To demonstrate superior performance compared to existing state-of-the-art algorithms.

Main Methods:

  • Utilized least absolute regression combined with two regularization techniques.
  • Developed a simplified model structure for ease of implementation and interpretability.
  • Implemented automatic temperature-sensitive variable selection.
  • Compared prediction accuracy and robustness against deep learning algorithms.

Main Results:

  • The proposed regularized regression model achieved the highest prediction accuracy.
  • The method demonstrated superior robustness compared to existing algorithms.
  • Automatic variable selection enhanced model efficiency.
  • Compensation experiments confirmed the model's practical effectiveness.

Conclusions:

  • The proposed regularized regression algorithm offers an effective and interpretable solution for CNC machine tool thermal error modeling.
  • This approach provides a practical alternative to complex deep learning methods, requiring less data and offering better insights.
  • The validated effectiveness paves the way for improved precision in CNC machining operations.