Abstract

Surface roughness is one of the most important attributes of paper and its measurement is normally used in quality control applications. Currently, measurement of surface roughness is done by using air leakage, optical, or subjective methods. In this article, an acoustical method for the determination of surface roughness of papers is described. The method is applied off-line and uses the friction noise recorded when a small brush slide over the surface of the paper. The analysis is done consider the friction noise ranging from 300 Hz to 10 kHz. Considering that friction noise is associated to surface roughness, a signal analysis algorithm was developed to take advantage of this fact. The algorithm consists of a frequency-domain feature extraction routine and an artificial neural network that represents the variation of the spectral features in a roughness scale. The results show that an affordable and efficient friction noise based system can be used for measuring surface roughness of tissue papers and other soft surfaces as an alternative to other methods. © 2009 Elsevier Ltd. All rights reserved.