1. Spectral assessment of surface topography
- Author
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Riemer, O., Nisbet, C., Phillips, D., Quagliotti, Danilo, Tuttolomondo, Cristiana, Maculotti, Giacomo, Genta, Gianfranco, Galetto, Maurizio, Hansen, Hans N., Riemer, O., Nisbet, C., Phillips, D., Quagliotti, Danilo, Tuttolomondo, Cristiana, Maculotti, Giacomo, Genta, Gianfranco, Galetto, Maurizio, and Hansen, Hans N.
- Abstract
The digital industrialization is revitalizing the way that manufactured products are conceived. The opportunity of several prevailing technologies allows products’ digital modelling, and enhanced productivity, with cost-efficient solutions. Even so, corresponding effective metrology methods are urgently needed to preserve the manufacturing digitalization whole perspective. In this background, an alternative assessment of optical surface topography measurements was demonstrated on micrographs’ spatial frequency content. Specifically, the power spectral density (PSD) was used for the evaluation of Sq and Sdq areal roughness parameters in a dedicated software developed in Matlab®. Initially, the dedicated software was validated against a commercial software. Afterwards, the calculation of Sq and Sdq parameters, as they are defined in the ISO 25178-2, was compared with the evaluation of the same parameters by the PSD. The results showed a broad agreement. It was found that differences were mostly due to the presence of noise. The evaluation of Sq and Sdq by the PSD is, in fact, less sensitive to noise. This was found above all for the Sdq parameter, where the approximation of the gradient in the conventional numerical evaluation can possibly enhance the influence of the noise. The PSD evaluation of Sq and Sdq was also proven more robust against different magnifications (namely different fields of view and pixel widths), retaining for the most unbiased statistical information for both 50× and 100× lens objectives. Thus, the PSD, and in general the frequency content of optical measurements, can be useful for the prediction of surface topographies based on digital modelling of measured data. It can be a manageable format of the acquired surfaces, less sensitive to noise and defects, to assist the digitalization of manufacturing.
- Published
- 2023