A neural network approach in the sensorial comfort of wool light fabrics by subjective and objective evaluation - Broega, A. C. (2010)
The textile and clothing industry, aware of the marketing evolution cannot neglect the requests of comfort, which have been increasing and are actual exigency for clothing goods consumers. There is an urgent need to evaluate and quantify the comfort properties of textile in general. The present work aims to make a study of the different types of lightweight wool fabrics, based on the objective evaluation of thermo and sensorial comfort, according to real preferences, to develop a simple sensorial comfort “predictable model”. The approach presented uses a self-organizing map (SOM), this type of neural network perform classification in a non-supervised fashion performing vector quantization and therefore placing similar vectors close together in the two dimensional output space. The unsupervised process leads to the self organization of modelling with no previous knowledge of what is being modelled and therefore it does not model a predetermined environment. Taking the above into account objective properties (physical and mechanical measured parameters) were selected and used to train the neural network to recognize subjective evaluations of sensorial comfort. The methodology applied in this work to develop the model is divided in two parts. In the first part, it was carried out the subjective evaluation of the materials, using a psychophysical methodology that enables the quantification of descriptive aspects of hand sensation (subjective evaluation by a panel of experts). One the other part, studies were conducted on fabric objective measurements: structural, thermal, physical and mechanical properties (KES-FB system). The results of the two parts were correlated by neural network techniques, in order to quantify the comfort, contributing for the definition of comfort sensorial standards for lightweight wool fabrics.