Predicting stain repellency characteristics of knitted fabrics using fuzzy modeling and surface response methodology

The effect of a stain repellent treatment on the water-oil repellency characteristics of plush knitted fabrics is investigated. We compared the efficiency of two methods of modeling; a Multicriteria analysis was employed by means of surface response method and an artificial intelligence-based system approach is presented by fuzzy logic modeling in which the effects of the operating parameters and intrinsic features of fabrics are studied. These parameters were pre-selected according to their possible influence on the outputs which were the contact angle and the air permeability. An original fuzzy logic-based method was proposed to select the most relevant parameters. The results show that air permeability was influenced essentially by knitted structure’s parameters but the variation of treatment parameters has a great effect on the contact angle. Thus, it is believed that artificial intelligence system could efficiently be applied to the knit industry to understand, evaluate, and predict water-oil repellency parameters of plush knitted fabrics more than Multicriteria analysis.