Error Separation Method for Geometric Distribution Error Modeling of Precision Machining Surfaces Based on K-Space...

Area of Science:

• Metrology and Precision Engineering
• Surface Metrology
• Signal Processing

Background:

• Geometric errors on component surfaces critically impact assembly accuracy and precision instrument stability.
• Accurate modeling of these errors is essential for performance prediction and process optimization.
• Existing methods struggle with effective separation of systematic and random error components.

Purpose of the Study:

• To propose a novel error separation method for geometric distribution errors on precision machining surfaces.
• To accurately distinguish between systematic and random errors using K-space spectrum analysis.
• To validate the method's effectiveness in preserving surface processing features.

Main Methods:

• Development of an error separation method based on the K-space spectrum.
• Utilization of a cruciform boundary line method within the K-space spectrum to define error boundaries.
• Separation of systematic and random errors based on frequency differences.

Main Results:

• The proposed K-space spectrum method successfully separates systematic and random geometric errors.
• Experimental verification on machined surfaces confirmed the method's efficacy.
• The method demonstrated superior performance in random error separation compared to common filtering techniques.

Conclusions:

• The K-space spectrum-based error separation method offers a robust approach for analyzing geometric errors in precision machining.
• This technique enhances the accuracy of error modeling, aiding in better performance prediction and process optimization.
• The method effectively preserves essential processing features while separating random errors.