Prediction of the tactile comfort of fabrics from functional finishing parameters using fuzzy logic and artificial neural network models

This paper aims to predict the hand values (HVs) and total hand values (THVs) of functional fabrics by applying the fuzzy logic model (FLM) and artificial neural network (ANN) model. Functional fabrics were evaluated by trained panels employing subjective evaluation scenarios. Firstly, the FLM was applied to predict the HV from finishing parameters; then, the FLM and ANN model were applied to predict the THV from the HV. The estimation of the FLM on the HV was efficient, as demonstrated by the root mean square error (RMSE) and relative mean percentage error (RMPE); low values were recorded, except those bipolar descriptors whose values are within the lowermost extreme values on the fuzzy model. However, the prediction performance of the FLM and ANN model on THV was effective, where RMSE values of ∼0.21 and ∼0.13 were obtained, respectively; both values were within the variations of the experiment. The RMPE values for both models were less than 10%, indicating that both models are robust, effective, and could be utilized in predicting the THVs of the functional fabrics with very good accuracy. These findings can be judiciously utilized for the selection of suitable engineering specifications and finishing parameters of functional fabrics to attain defined tactile comfort properties, as both models were validated using real data obtained by the subjective evaluation of functional fabrics.