&lt;title&gt;Measuring roughness with dichromatic speckle correlation&lt;/title&gt;

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 subimages. 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.

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“…Usually, two lasers are used to measure the SSC 5,3,4 . We are using a C-Wave VIS Low Power which is a tunable laser from Hübner Photonics.…”

Section: Methodsmentioning

confidence: 99%

“…Although speckle metrology for surface topography measurements has been studied in great detail [3][4][5][6][7] , there are no industrial sensors available based on this principle. Recent advances in laser and camera technologies made this approach even more promising.…”

Section: Introductionmentioning

confidence: 99%

Surface roughness measurement of large areas with high spatial resolution by spectral speckle correlation

Laux

Schiller

Bertz

et al. 2023

Optical Measurement Systems for Industrial Inspection XIII

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“…Thus, it is recommended that multiple parameters of the surface roughness of machined surfaces should be studied in order to gain a deep insight intothe implications of machining parameters on surface quality [6]. In some cases, special techniques must beemployed in order to obtain a more accurate estimation of the roughness parameters' values [7], especially online, which is a challenging task [8][9][10].…”

Section: Introductionmentioning

confidence: 99%

Evaluation of the Feasibility of the Prediction of the Surface Morphologiesof AWJ-Milled Pockets by Statistical Methods Based on Multiple Roughness Indicators

Karkalos,

Thangaraj,

Karmiris-Obratański

2024

Surfaces

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Improvement of the surface quality of machined parts is essential in order to avoid excessive and costly post-processing. Although non-conventional processes can efficiently carry out the machining of difficult-to-cut materials with high productivity, they may also, for various reasons, be related to increased surface roughness. In order to optimize the surface quality of generated surfaces in a reliable way, surface profiles obtained during these processes must be adequately modeled. However, given that most studies have focused on Ra or Rz indicators or are based on the assumption of a normal distribution for the profile heights, relevant models cannot accurately represent the surface characteristics that exist in a real machined surface with a high degree of accuracy. Thus, in the present study, a new modeling approach based on the use of a statistical probability distribution for the surface profile height is proposed. After six different distributions were evaluated on the basis of a three-stage procedure involving different roughness indicators pertaining to the abrasive waterjet (AWJ) milling of pockets, it was found that, although it is not possible to model the nominal values of every roughness parameter simultaneously, in several cases, it is possible to approximate the values of critical indicators such as Ra, Rz, Rsk, Rku and Rp/Rv ratio by Weibull distribution with a sufficient degree of accuracy.

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