Ultraviolet protection factor (UPF) of woven fabrics is modeled by using two soft computing approaches, namely adaptive network based fuzzy inference system (ANFIS) and artificial neural network (ANN). Three fabric parameters: proportion of polyester in weft yarns, weft count, and pick density are used as input parameters for predicting fabric UPF. Two levels (low and high) of membership function for each of the input parameters are used to reduce the complexity of ANFIS. The eight linguistic fuzzy rules trained by ANFIS are able to explain the relationship between fabric parameters and UPF. A comparison between ANFIS and ANN models is also presented. Both the models predict the UPF of fabrics with very good prediction accuracy in the testing data sets.

The developed equations relating fabric cover and UPF can be used as a primary guideline while selecting fabrics for UV protection.

Zinc oxide (ZnO) nanostructures have been grown on p‐aramid (Kevlar) fabric to improve interfacial properties of reinforced composites. Whiskerization technique was adopted to develop ZnO nanostructures, like an array of whiskers, on the surface of Kevlar fabric. Hydrothermal process was used which involves three steps i.e. functionalization, seed layer formation and growth of nanorods. Hexamethylenetetramine and zinc nitrate were used to grow ZnO nanostructures. It was found that pressure controlled autoclave was the best method for generation of nanostructures. It was also observed that the material (fabric) to solution ratio (M:S) significantly affected the morphology and density of developed nanostructures. Incorporation of ZnO nanostructures on Kevlar fabric improved the tensile and impact properties of Kevlar fabric and its composites. The deposition of nanostructures improved the interfacial properties by causing fibers to interlock with matrix thus allowing the formation of a graded interface between the two phases. POLYM. COMPOS., 39:3300–3306, 2018. © 2017 Society of Plastics Engineers

Optimization of woven fabric parameters for ultraviolet protection factor (UPF) and comfort properties has been attempted using hybrid artificial neural network (ANN)-genetic algorithm (GA) system. ANN was used for developing the prediction models, and GA was employed as an optimization tool. Four feasible combinations of UPF, air permeability and moisture vapor transmission rate (MVTR) were chosen from the Pareto charts of UPF-air permeability and UPF-MVTR. Penalty function method was adopted to form a single objective function by combining the objectives and constraints related to UPF, air permeability and MVTR. The developed ANN-GA hybrid system was executed to obtain the solution set of input parameters for achieving the targeted fabric properties. To validate the developed ANN-GA-based fabric parameter optimization system, four fabric samples were woven using the solution sets of input parameters and functional properties of these engineered fabrics were evaluated. The targeted and achieved values of fabric properties of four validation samples were in reasonably good agreement.

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