Hybrid optimization algorithm for thermal displacement compensation of computer numerical control machine tool using...

Area of Science:

• Manufacturing Engineering
• Thermal Analysis
• Control Systems

Background:

• Computer numerical control (CNC) machines experience thermal drift due to heat generated during operation, impacting machining accuracy.
• Factors like ambient temperature, cutting heat, friction, material properties, and machining duration influence thermal deformation and tooltip displacement.

Purpose of the Study:

• To develop and validate a hybrid optimization algorithm for managing thermal variables in CNC machine tool spindles.
• To model and compensate for spindle thermal errors, thereby enhancing precision in manufacturing processes.

Main Methods:

• A hybrid approach combining regression analysis and fuzzy inference was employed to model spindle thermal behavior.
• Spindle speed and 16 temperature measurements served as inputs, with axial thermal error as the output.
• Regression equations were developed for different speeds to capture varying thermal rise slopes.

Main Results:

• The proposed hybrid thermal displacement compensation framework significantly reduced errors caused by spindle temperature variations.
• The model demonstrated adaptability to environmental changes by limiting the machining speed range, reducing data requirements and adaptation time.
• This compensation strategy was shown to indirectly improve product yield.

Conclusions:

• The hybrid optimization algorithm effectively mitigates thermal displacement errors in CNC machine spindles.
• The developed framework offers a robust solution for enhancing machining accuracy and process efficiency under varying conditions.
• Adaptability and reduced data needs make the model practical for industrial implementation, leading to improved manufacturing outcomes.