# Paper Tissue Softness Rating by Acoustic Emission Analysis
###### Abstract  
Softness is one of the essential properties of hygiene tissue products. Reliably measuring it is of utmost importance to ensure the balance between customer expectations and cost-effective tissue production. This study presents a method for assessing softness by analyzing acoustic emissions produced while tearing a tissue specimen. The aim was to train neural network models using the corrected results of human panel tests as the ground truth labels and to predict the tissue softness in two- and three-class recognition tasks. We also investigate the possibility of predicting some production parameters related to the softness property. The results proved that tissue softness and production parameters could be reliably estimated only by the tearing noise. acoustic emission; machine learning; tissue softness analysis +
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samples with fixed scores (scoring method). Another way to evaluate is where the panelists are asked to rank the tissue samples from least to most soft by a pair-wise comparison (ranking method). However, softness as subjective perception is difficult to define and quantify, and the panel tests are a time and cost-intensive process, particularly in the case of a large number of samples with fewer references [5]. A factor that further complicates the softness assessment is that many tissue manufacturers do not publish their evaluation procedures. The test parameters could differ only slightly, but comparability among them is impossible. Recently, many analytical methods were developed to instrumentally quantify the paper properties, which can be used individually or in combination to correlate with softness. For instance, strength, stiffness, and softness typically depend on sheet density and creeping geometry; therefore, exploring the correlation between the physical properties contributes to reliable softness modeling. An extensive overview of the tissue softness measurements is given in [1], divided into Direct Measures of Softness, Prediction Models for Correlating with Softness, and Indication of Surface Topography. Recent advances use a combined approach of custom softness measurement equipment and integrated algorithms that effectively characterize softness, especially after calibration. The idea, that acoustic emission analysis (AE) can characterize the paper's physical properties during the destruction of the fiber-to-fiber bonds, the fiber, or the fiber deformation, has been present for a long time [6]. Many studies showed that the different fiber types produce different sound frequencies, and the frequency analysis shows that weak bonds are loosened first, then the stronger bonds, and in the end, the fibers [7]. Papers with increased latex content show higher strength combined with a slight increase in acoustic emissions [8]. In the study [9], the authors investigated the micro mechanics behind the behavior of paper under mechanical loading by AE monitoring that was used for detecting and recording damage. In [10], statistical analysis of acoustic emission data from tensile experiments on paper sheets was conducted, with samples broken under strain control. In [11], acoustic emissions were used to investigate the development of damage in paper specimens. The preliminary study [12] investigated the possibility of distinguishing between different types of paper tos by analyzing the tearing noise. Hidden Markov Models were trained on sounds of five different types of papers, and approximately 75% of the samples were correctly recognized. The acoustic emission also can be produced by converting vibrations caused by friction of the fabric surface into an acoustic spectrum. The tissue softness analyzer (TSA) [13] measures three decisive properties: fiber softness (TS7), texture (TS750), and stiffness (D). From these measured variables, as well as thickness, grammage, and the number of plies, a hand-feel (HF) value is calculated using mathematical models.