Previous work on fabric band evaluation is reviewed. By using a multiple regression approach, four models, namely, a linear model, Webber-Fechner’s law, Kawabata-Niwas’s law and Stevens’s law, for primary haud prediction have been compared. The results show that deviations from Stevens’s law are much smaller than those from the other three models. Accordingly, Stevens's law has been selected for the prediction of primary hand values. Equations for stiffness, smoothness, and softness and fullness prediction are derived by using the data from 39 worsted fabrics. Confidence intervals for tile parameter estimates and other statistical parameters are given. In addition, a psychophysical explanation for the process of fabric hand evaluation, which is a basis of the selection of Stevens’s law, is made.
ASJC Scopus subject areas
- Materials Science (miscellaneous)
- Agricultural and Biological Sciences(all)
- Polymers and Plastics
- Industrial and Manufacturing Engineering