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This study, it was aimed to determine the equations and models for estimating the pilling propensity of interlock knitted fabrics. Seven different cotton blends supplied from different spinning mills, yarns in 3 different yarn counts (Ne 20, Ne 30 and Ne 40) and in 3 different twist coefficients (αe=3.2, 3.6 and 4.0) were produced. Interlock knitted fabrics were manufactured in three different fabric tightness values from each of the produced yarns. The pilling tendencies of the fabrics were tested according to EN ISO 12945–2 standard by a Martindale pilling and abrasion device. The PillGrade Objective Pilling Grading System, based on the image analysis principle, was used for evaluating the pilling propensity of the fabrics. By using this system, the pilling degree of the fabrics, total pill number, total weighted pill number, average pill area, and average pill height of the fabrics were measured. Fiber features determined by an AFIS PRO 2 instrument with the samples taken from the cotton roving were used as independent variables for the regression analysis. Moreover, yarn unevenness, yarn twist, yarn count, yarn hairiness, and fabric cover factor values were included in the equations as independent variables; and by considering each of the pilling features measured by PillGrade as a dependent variable, multivariate linear regression equations were determined and the availability of the equations was investigated in detail statistical analyses.

Artificial neural network (ANN) is a mathematical model inspired by biological neural networks and it processes information using a connectionist approach to computation. The aim of the second part of the study is to determine models for estimating the pilling propensity of the interlock knitted fabrics produced from yarns of different yarn counts (Ne 20, Ne 30, Ne 40) and yarn twist coefficients (αe=3.2, αe=3.6, αe=4.0) spun by using seven different cotton types harvested from different regions. The fabrics were manufactured in three different tightness factors, including dense, medium, and loose, by changing the yarn length utilized in each course of the fabrics. The models for pilling degree, total pill number, total weighted pill number, average pill area, and average pill height of the fabrics evaluated by PillGrade Objective Pilling Grading System, were derived by using a neural network method. In order to define the effective properties on pilling formation, sensitivity analysis was carried out. All models indicated relatively good estimation power. Fabric cover factor and short fiber content were found as the most significant parameters influencing the pilling propensity feature of the interlock knitted fabrics.