Fabric hand is commonly adopted for assessing fabric quality and prospective perfor mance in a particular end use. In general, fabric hand is primarily assessed subjectively. Subjective assessments treat fabric hand as a psychological reaction obtained from the sense of touch, based on the experience and sensitivity of humans. It is very difficult to predict such psychological perceptions of hand based on fabric properties. In this paper, we identify reliable sensory fabric hand attributes with correlated attributes of fabric properties, and we attempt a novel approach for predicting sensory hand based on fabric properties using a resilient back-propagation neural network. In this study, we assess forty woven fabrics to determine twelve significant fabric properties and fourteen reliable attributes of sensory hand. Our proposed system performs at a very low mean square error after fine tuning. Five extra woven fabrics are used to show that the performance of such a prediction system closely agrees with subjective test results. Our proposed system can allow field practitioners to evaluate their fabrics more closely to match with customers' expectations.
ASJC Scopus subject areas
- Chemical Engineering (miscellaneous)
- Polymers and Plastics