Classification of Visual Smoothness Standards Using Multi-Scale Areal Texture Parameters and Low-Magnification...

Area of Science:

• Surface Engineering and Materials Science
• Computational Metrology and Surface Characterization

Background:

• Objective specification of surface finish is crucial for consistent visual appearance in surface coating engineering.
• Existing methods may lack the precision required for detailed surface finish grading.

Purpose of the Study:

• To demonstrate the utility of the surface quality and inspection descriptors (SQuID™) computational framework.
• To effectively rank and classify different grades of powder-coated surface finish appearances.
• To compare the performance of SQuID™ with traditional gloss meter measurements.

Main Methods:

• Utilized ISO 25178-2 areal surface metrics derived from bandpass-filtered measurements.
• Employed a coherent scanning interferometer to capture data from ten visual smoothness standards.
• Compared multi-scale areal texture parameters from SQuID™ against hand-held gloss meter data.

Main Results:

• The SQuID™ framework successfully classified ten different powder-coated surface finishes.
• Classification accuracies ranged from 65% to 99%, contingent on filtering and parameter selection.
• Optimal classification was achieved using only five multi-scale topography descriptions.

Conclusions:

• The SQuID™ computational framework provides an effective tool for objective surface finish quantification.
• Multi-scale areal texture parameters offer a robust method for classifying surface smoothness.
• SQuID™ demonstrates superior or comparable performance to traditional gloss meters for surface finish grading.