Prediction of wool knitwear pilling propensity using support vector machines

The propensity of wool knitwear to form entangled fiber balls, known as pills, on the surface is affected by a large number of factors. This study examines, for the first time, the application of the support vector machine (SVM) data mining tool to the pilling propensity prediction of wool knitwear. The results indicate that by using the binary classification method and the radial basis function (RBF) kernel function, the SVM is able to give high pilling propensity prediction accuracy for wool knitwear without data over-fitting. The study also found that the number of records available for each pill rating greatly affects the learning and prediction capability of SVM models.

Predicting the pilling tendency of wool knits

This work investigates the application of artificial neural network modeling (ANN) to model the relationships between fiber, yarn, and fabric properties and the pilling propensity of single jersey and rib pure wool knitted fabrics based on the ICI Pilling Box method. Validation of the model on an independent validation data set suggests that the accurate prediction of pilling propensity is possible with the best performing model achieving a correlation with the subjectively rated pilling grades of approximately 85%. Importantly, it is also illustrated that a larger training set can lead to a marked improvement in the accuracy of predictions.

Prediction of wool knitwear pilling propensity using data mining techniques

This thesis examined the application of data mining techniques to the issue of predicting pilling propensity of wool knitwear. Using real industrial data, a pilling propensity prediction tool with embedded trained support vector machines is developed to provide high accuracy prediction to wool knitwear even before the yarn is spun!