Ranking fibre and process parameters affecting thermal resistance of needle-punched blankets using neural network model

In this paper, an artificial neural network (ANN) model has been designed to predict the thermal resistance of needle‐punched blankets. Web‐laying (parallel‐ and cross‐laid), fibre fineness, fibre degree of hollowness, fabric weight, depth of needle penetration and needle punch density are considered as input parameters to predict the thermal resistance of needle‐punched nonwoven blankets. In order to reduce the dependency of the results on a specific partition of the data into training and testing sets, three‐way cross validation tests were performed, that is, total data were divided into training and testing sets in three different ways. The predicted thermal resistance correlated well with the experimental thermal resistance. The relative contribution of each parameter to the overall prediction of the thermal resistance was studied by carrying out a sensitivity analysis of the test data set. The results of sensitivity analysis show that web‐laying is the most important input parameter, followed by depth of needle penetration, fabric weight, degree of fibre hollowness, needle punch density and fibre fineness.