Rough Surface Characterization Parameter Set and Redundant Parameter Set for Surface Modeling and Performance...

Area of Science:

• Surface Metrology
• Materials Science
• Data Mining

Background:

• Current characterization parameter sets (CPS) for surface roughness lack scientific rigor and generalizability.
• Empirical selection and small-scale experiments limit the effectiveness of existing CPS.
• Confusion in application arises from the non-unified nature of current surface performance parameters.

Purpose of the Study:

• To propose a novel, data-driven method for constructing a scientifically sound and unified Characterization Parameter Set (CPS) for rough surfaces.
• To address the limitations of empirical and small-sample-based CPS construction.
• To establish a robust parameter set for accurate surface performance characterization.

Main Methods:

• Utilized data mining techniques and statistical theory to analyze 26 roughness parameters from ISO 25178.
• Employed reconstructed surfaces and machining experiments to validate parameter relevance.
• Divided parameters into a core Characterization Parameter Set (CPS) and a Redundant Parameter Set (RPS), establishing mapping relationships.

Main Results:

• Identified that 50% of the analyzed roughness parameters constitute the Redundant Parameter Set (RPS).
• The newly constructed Characterization Parameter Set (CPS) is of significant importance for surface performance.
• The proposed CPS effectively covers all essential surface topography information.

Conclusions:

• The developed method provides a scientifically validated and unified Characterization Parameter Set (CPS) for rough surfaces.
• This new CPS enhances accuracy and reduces application confusion in surface performance studies.
• The findings offer more effective parameters for evaluating workpiece surface performance within batches.