An unambiguous cloudiness index for nonwovens
This provides a more precise tool for industrial quality assurance in nonwoven and paper manufacturing, but it is incremental as it builds on existing mathematical foundations.
The paper tackled the problem of quantifying cloudiness in nonwovens by deriving a cloudiness index based on the power spectrum approach, showing it is easy to measure and carries more information than alternatives like the Laplacian pyramid.
Cloudiness or formation is a concept routinely used in industry to address deviations from homogeneity in nonwovens and papers. Measuring a cloudiness index based on image data is a common task in industrial quality assurance. The two most popular ways of quantifying cloudiness are based on power spectrum or correlation function on the one hand or the Laplacian pyramid on the other hand. Here, we recall the mathematical basis of the first approach comprehensively, derive a cloudiness index, and demonstrate its practical estimation. We prove that the Laplacian pyramid as well as other quantities characterizing cloudiness like the range of interaction and the intensity of small-angle scattering are very closely related to the power spectrum. Finally, we show that the power spectrum is easy to be measured image analytically and carries more information than the alternatives.