Year-Round Thermal Error Modeling and Compensation for the Spindle of Machine Tools Based on Ambient Temperature...

Area of Science:

• Mechanical Engineering
• Manufacturing Technology
• Metrology

Background:

• Thermal errors significantly impact machine tool accuracy.
• Existing thermal error models struggle with seasonal ambient temperature fluctuations.
• Robustness and prediction accuracy are key performance indicators for thermal error compensation.

Purpose of the Study:

• To propose a year-round thermal error modeling and compensation method for machine tool spindles.
• To enhance prediction accuracy and robustness despite seasonal temperature variations.
• To establish an optimal ambient temperature interval (ATI) strategy.

Main Methods:

• Utilized the C-Means clustering algorithm to determine applicable ambient temperature intervals (ATIs).
• Established thermal error prediction models (TEPMs) for different ATIs.
• Analyzed the prediction effect of varying numbers of ATIs to find the optimal quantity.
• Validated the ATI model against experimental data across the entire year.

Main Results:

• The proposed ATI model demonstrated superior prediction accuracy compared to low and high ambient temperature models (20.6% and 41.7% higher, respectively).
• Robustness was significantly improved by 48.8% and 62.0% compared to the low and high ambient temperature models.
• The ATI method effectively addresses seasonal temperature variations.

Conclusions:

• The proposed ambient temperature interval (ATI) method provides a robust and accurate solution for year-round thermal error compensation in machine tools.
• This approach overcomes the limitations of traditional models in handling seasonal temperature changes.
• The ATI method is crucial for maintaining high machine tool accuracy throughout the year.