Fuzzy logic model expansion and optimization for predicting polyester cut-pile carpet thickness-loss

Abstract

Thickness-loss is the most common problem in carpet after static and dynamic loading. Pile yarn properties as well as carpet structural parameters are mainly responsible for carpet thickness-loss. In the present research, an advanced version of recently developed fuzzy logic model is introduced. The model is able to predict thickness-loss of polyester carpets based on carpet pile density, pile height, and pile yarn count. Experimental work was performed to provide data for model knowledge base. Genetic algorithm was employed to optimize the fuzzy logic model parameters. Finally, lowest possible thickness-loss value together with corresponding values of carpet and pile yarn parameters bring this result was defined, using developed model. Modeling results showed that the model attained correlation coefficients as 0.9932, 0.9911, 0.9950, and 0.9957 between predicted and experimental values of carpet thickness-loss after low and high dynamic loading and static loading with short and long relaxation times, respectively. On the other hand, model predictions in four new unsighted conditions have brought correlation coefficients as 0.82, 0.89, 0.88, and 0.90 for low and high dynamic loading and static loading after short and long relaxation times, respectively. These results denote acceptable reliability of new developed model. Eventually, it is defined that levels of 850, 7.5, and 957.5 for carpet pile density, pile height, and pile yarn linear density, respectively, bring minimum carpet thickness-loss.