The robustness of objective fabric pilling evaluation method

Previously, we proposed a new method to identify fabric pilling and objectively measure fabric pilling intensity based on the two-dimensional dual-tree complex wavelet reconstruction and neural network classification. Here we further evaluate the robustness of the method. Our results indicate that the pilling identification method is robust to significant variation in the brightness and contrast of the image, rotation of the image, and 2 i (i is an integer) times dilation of the image. The pilling feature vector developed to characterize the pilling intensity is robust to brightness change but is sensitive to large rotations of the image. As long as all fabric images are adjusted to have the same contrast level and the sample is illuminated from the same direction, the pilling feature vectors are comparable and can be used to classify the pilling intensity.

New methods for objective evaluation of fabric pilling by frequency domain image processing

Fabric pilling is a serious problem for the apparel industry. Resistance to pilling is normally tested by simulated accelerated wear and manual assessment of degree of pilling based on a visual comparison of the sample to a set of test images. A number of automated systems based on image analysis have been developed. The authors propose new methods of image analysis based on the two-dimensional wavelet transform to objectively measure the pilling intensity in sample images. Initial work employed the detail coefficients of the two-dimensional discrete wavelet transform (2DDWT) as a measure of the pilling intensity of woven/knitted fabrics.

This method is shown to be robust to image translation and brightness variation. Using the approximation coefficients of the 2DDWT, the method is extended to non-woven pilling image sets. Wavelet texture analysis (WTA) combined with principal components analysis are shown to produce a richer texture description of pilling for analysis and classification. Finally, employing the two-dimensional dual-tree complex wavelet transform as the basis for the WTA feature vector is shown to produce good automated classification on a range of standard pilling image sets.

A comparison of the ICI Pillbox and the Random Tumble Methods is assessing pilling and appearance change of worsted spun cashmere and cashmere-wool blend knitwear

This study evaluated the differences between two international test methods on the assessment of pilling and appearance change of worsted spun cashmere and superfine wool knitwear and their blends. Differences between the standard ICI Pill Box Method and the Random Tumble Method were found in both the significance and magnitude of resistance to pilling and appearance change and the amount of fabric mass loss of worsted spun cashmere and cashmere superfine wool blend knit fabrics. The ICI Pill Box Method differentiated to a greater extent the effects of wool type and blend ratio of cashmere and wool compared with the Random Tumble Method. Generally the addition of cashmere or low crimp superfine wool resulted in fabrics being more resistance to pilling and appearance change compared with fabrics made from high crimp superfine wool. This was associated with increased fabric mass loss when assessed by the ICI Pill Box Method but not with the Random Tumble Method. KEYWORDS: Cashmere, crimp, wool, pilling, appearance change, knitwear

Assessment of the pilling propensity of wool knitwear

This study addresses a major issue facing the wool industry - the formation of entangled fibres or pills on wool knitwear. By examining the factors that contribute to the inconsistent pilling results, ways of improving the test procedures have been identified. This will have practical implications for the textile industry.

Controlling and predicting the pilling propensity of knitted wool fabrics

This thesis tackles an important quality issue in the wool industry - the pilling of wool knitwear. Through artificial neural network modelling, the important fibre, yarn and fabric attributes that affect fabric pilling have been identified. A predictive model on fabric pilling has been developed, which will assist the wool industry in the management and control of fabric pilling.

Objective evaluation of fabric pilling propensity

Fabric pilling is of critical importance to the textile industry. This study developed an objective pilling evaluation method using computer image techniques. The applicability and robustness of the proposed method were investigated based on actual knitted fabrics, the results are very accurate and repeatable.

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.

Performance of an objective fabric pilling evaluation method

In previous work, we established the principle of objective fabric pilling evaluation based on two-dimensional dual-tree complex wavelet transform (2DDTCWT) image reconstruction and non-linear classification using a neural network. This proof-of-principle work was performed using standard pilling test images. Here, we demonstrate the practical operation of the objective pilling evaluation method using a large set of real fabric pilling samples. We show that piling classification results from a trained multiple-layer perceptron neural network achieve a regression correlation of approximately 96% with the corresponding human expert pilling ratings.

Objective pilling evaluation of nonwoven fabrics

A pilled nonwoven fabric image consists of brightness variations caused by high frequency noise, randomly distributed fibers, fuzz and pills, fabric surface unevenness, and background illumination variance. They have different frequency and space distributions and thus can be separated by the two-dimensional dual-tree complex wavelet transform reconstructed detail and approximation images. The energies of the six direction detail sub-images, which capture brightness variation caused by fuzz and pills of different sizes, quantitatively characterize the pilling volume distribution at different directions and scales. They are used as pilling features and inputs of neural network supervised classifier. The initial results based on a nonwoven wool fabric standard pilling test image set, the Woolmark®‚ SM 50 Blanket set, suggest that this objective pilling evaluation method developed by the combination of pilling identification, characterization method and neural network supervised classifier is feasible.

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!

An integrated method of feature extraction and objective evaluation of fabric pilling

A pilled fabric image consists of sub-images of different frequency components, and the fabric texture and the pilling information are in different frequency bands. Interference from fabric background texture affects the accuracy of computer-aided pilling ratings. A new approach for pilling evaluation based on the multi-scale two-dimensional dualtree complex wavelet transform (CWT) is presented in this paper to extract the pilling information from pilled fabric images. The CWT method can effectively decompose the pilled fabric image with six orientations at different scales and reconstruct fabric background texture and pilling sub-images. This study used an energy analysis method to search for an optimum image decomposition scale and dynamically discriminate pilling image from noise, fabric texture, fabric surface unevenness, and illuminative variation in the pilled fabric image. For pilling objective rating, six parameters were extracted from the pilling image to describe pill properties. A Levenberg-Marquardt backpropagation neural rule was used as a classifier to classify the pilling grade. The proposed method was evaluated using knitted, woven, and nonwoven pilled fabric images photographed with a digital camera.

Reducing the pilling propensity of wool knits with a three-step plasma treatment

A three-step plasma treatment, including surface activation with argon, surface functionalization with oxygen and then thin film deposition using a pulsed plasma polymerization of hexamethyldisiloxane (HMDSO), was used in low-pressure plasma to improve the pilling resistance of knitted wool fabric. The pilling propensity of the treated samples was investigated and compared with the pilling propensity of untreated, argon activated and oxygen functionized samples and argon and oxygen plasma-treated samples that were afterwards subject to continuous wave plasma polymerization of HMDSO. With the three-step treatment, a pilling grade of four was achieved for the treated wool fabric, while that of untreated and other plasma-treated was two and three, respectively. For the three-step plasma-treated sample, a uniform HMDSO polymer coating of 300 nm thickness was obtained; X-ray photoelectron spectroscopy (XPS) showed the presence of the silicone element, and Fourier transform infrared (FTIR) spectroscopy confirmed the chemical structure of the coating. No apparent differences were found in the whiteness index between the treated and untreated wool knits, but there was deterioration in the bursting strength and handle of the plasma-treated wool samples.

Mechanistic modelling of pilling. Part II: Individual-fibre computational model

A one-dimensional computational model of pilling of a fibre assembly has been created. The model follows a set of individual fibres, as free ends and loops appear as fuzz and arc progressively withdrawn from the body of the assembly, and entangle to form pills, which eventually break off or are pulled out. The time dependence of the computation is given by ticks, which correspond to cycles of a wear and laundering process. The movement of the fibres is treated as a reptation process. A set of standard values is used as inputs to the computation. Predictions arc given of the change with a number Of cycles of mass of fuzz, mass of pills, and mass removed from the assembly. Changes in the standard values allow sensitivity studies to be carried out.

Mechanistic modelling of pilling. Part I: Detailing of mechanisms

Mechanistic models of pilling are discussed in general terms, and a framework for pilling simulations is thereby created. A fundamental flaw in earlier models of pilling is revealed. A more comprehensive model of fibre diffusion and withdrawal from the fabric is proposed, and this is solved in general terms to find the rate of fuzz growth. Fuzz wear-off and entanglement into pills are discussed. Fibre fatigue is introduced, and it is demonstrated that this potentially increases the rate of withdrawal of anchor fibres.