Presentation + Paper
15 August 2023 Surface roughness measurement of large areas with high spatial resolution by spectral speckle correlation
Patrick Laux, Annelie Schiller, Alexander Bertz, Daniel Carl, Stephan Reichelt
Author Affiliations +
Abstract
Roughness is one of the most important surface quality parameters in metal sheet processing. Thus contact-free optical methods are highly interesting. Using the innovative approach of spectral speckle correlation (SSC) has the potential to measure the spatial roughness distribution of large surfaces quickly and thus inline. In SSC, speckle patterns are recorded at different wavelengths and correlated with each other to determine the roughness of the sample. We show that the relationship between roughness and correlation coefficient as a function of wavelength difference is valid over a large range. The roughness of larger surfaces can be determined by separate evaluation of sub-images. We present the importance of the sub-image size to get reliable, reproducible measurements relating to existing standards. In this work we show how to measure the roughness parameter Sa for values ranging from 0.59 μm to 7.75 μm with a spatial resolution below 1 mm by SSC. We demonstrate that the theory is valid over a large range of wavelength differences. This is shown using wavelength differences between 0.2 nm and 93 nm in the visible spectrum.
Conference Presentation
© (2023) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Patrick Laux, Annelie Schiller, Alexander Bertz, Daniel Carl, and Stephan Reichelt "Surface roughness measurement of large areas with high spatial resolution by spectral speckle correlation", Proc. SPIE 12618, Optical Measurement Systems for Industrial Inspection XIII, 126181F (15 August 2023); https://doi.org/10.1117/12.2672865
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KEYWORDS
Speckle

Surface roughness

Tolerancing

Speckle pattern

Holography

Sensors

Polarized light

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