Modeling the relationship between tactile sensation and physical properties of synthetic leather

Abstract

Surface texture plays an important role in expressing the tactile sensation of leather. The purpose of this study was to develop a combinational panel of physical properties that express the tactile sensations of coarse/fine, uneven/flat, soft/hard, sticky/slippery, and moist/dry. Thirteen synthetic leather samples were fabricated from polyurethane, and eight physical properties were measured as storage modulus, loss modulus, surface roughness, waviness, mean frictional coefficient, apparent contact angle, contact angle hysteresis, and urethane thickness. Thirty adult women performed sensory evaluation of the five tactile sensations by using a semantic differential method based on a five-point scale. The tactile sensation–physical property model for each tactile sensation was explored with logistic regression analysis. Receiver operating characteristic analysis showed that the proposed models based on multiple physical properties clearly distinguish the tactile sensations with higher sensitivity and specificity compared to using a single physical property. The dynamic viscoelasticity contributed to all the tactile sensations when polyurethane was used as synthetic leather. Additionally, the difference of five tactile sensations might be caused by the surface roughness, the mean frictional coefficient, and the polyurethane thickness.

Effect of calcium carbonate particle size and content on the thermal properties of PVC foamed layer used for coated textiles

“…The coating is increasingly becoming an important way of adding value to textiles. It aims to develop the functional properties of textiles, improve certain characteristics, and guarantee that fabrics meet parameters of performance that will not be achievable from uncoated and leathered fabrics [ 1 ].…”

Facial cleansing wipes: A predictive model between consumer perception and in vitro lab measurements for rapid prototype screening

“…Other techniques such as logistic regression work when the variables are not viewed as continuous. For example, the sensory properties of artificial leather were related to physical methods using logistic regression [32]. Artificial neural networks (ANNs) is an alternative statistical technique that works well with nonlinear data sets [23–34].…”

“…For example, the sensory properties of artificial leather were related to physical methods using logistic regression [32]. Artificial neural networks (ANNs) is an alternative statistical technique that works well with nonlinear data sets [23–34]. ANNs is regarded to be more intelligent than the traditional statistical regression methods due to its robustness to noise and inconsistencies in the data, and handling of complex data sets [35,36].…”