Tokat, Sezai2024-05-222024-05-222007-020941-06431433-3058https://link.springer.com/article/10.1007/s00521-006-0048-8https://hdl.handle.net/11452/41517The abrasion resistance of chenille yarn is crucially important in particular because the effect sought is always that of the velvety feel of the pile. Thus, various methods have been developed to predict chenille yarn and fabric abrasion properties. Statistical models yielded reasonably good abrasion resistance predictions. However, there is a lack of study that encompasses the scope for predicting the chenille yarn abrasion resistance with artificial neural network (ANN) models. This paper presents an intelligent modeling methodology based on ANNs for predicting the abrasion resistance of chenille yarns and fabrics. Constituent chenille yarn parameters like yarn count, pile length, twist level and pile yarn material type are used as inputs to the model. The intelligent method is based on a special kind of ANN, which uses radial basis functions as activation functions. The predictive power of the ANN model is compared with different statistical models. It is shown that the intelligent model improves prediction performance with respect to statistical models.eninfo:eu-repo/semantics/closedAccessAbrasion resistanceRadial basis functionsArtificial neural networksChenille yarnPredictionComputer scienceArticle0002441999000042-s2.0-33847294760139145162https://doi.org/10.1007/s00521-006-0048-8Computer science, artificial intelligenceYarns; Cotton Fibers; Weft