Wear attributes of cashmere single jersey knitted fabrics blended with high and low crimp superfine merino wool

In a replicated experiment, we investigated the impact of cashmere in blends with superfine wools on the wear attributes of single jersey knitted fabrics. We also investigated the relative performance of low crimp/low fiber curvature superfine wool when compared with cashmere and also when compared with traditional high crimp/high fiber curvature superfine wool in pure and blended knitted fabrics. Wool type, blend ratio and fabric structure affected fabric air permeability, resistance to pilling and change in appearance, relaxation shrinkage, hygral expansion, and dimensional stability during laundering. The responses to variation in fiber crimp were much greater than previously reported. The fabric properties of low crimp wool differed significantly from those made from high crimp wool, and low crimp wool fabric properties differed significantly from, but were closer to, the fabric properties of cashmere, compared with high curvature wool.

Predicting comfort properties of knitted fabrics by assessing yarns with the Wool ComfortMeter

This study examined the feasibility of assessing yarns with the Wool ComfortMeter (WCM) to predict the comfort properties of the corresponding single jersey-knitted fabrics. The optimum yarn arrangement to predict the comfort value of a corresponding control fabric was determined using nine wool and wool/nylon-blended yarns (mean fibre diameter range 16.5–24.9 μm) knitted into 34 different fabrics. Using a notched template, yarn winding frequencies of 1, 3, 6, 12, 25 and 50 parallel yarns were tested on the WCM. The best predictor of fabric WCM values was using 25 parallel yarns. Inclusion of knitting gauge and cover factor slightly improved predictions. This indicates that evaluation at the yarn stage would be a reliable predictor of knitted fabric comfort, and thus yarn testing would avoid the time and expense of fabric construction.

Physical properties of novel co-woven-knitted fabrics

 Co-woven-knitted (CWK) fabrics have been reported previously. Historically these unique structures have been used to develop composite and shielding fabrics. In this study, novel CWK structures with unique appearances was developed with a modified machine using wool and polyester yarns. The physical properties of these fabrics were compared with conventional woven and knitted fabrics. The thickness of the CWK fabrics was similar to knits. The fabrics showed a unique tensile strength, with higher bending rigidity, and performed better in abrasion resistance.

Effects of variation in wool fiber curvature and yarn hairiness on sensorial assessment of knitted fabrics

Previous investigations have shown that prickle discomfort sensations of wool fabrics are primarily determined by the mean fiber diameter of the wool. It is also known that differences in wool fiber curvature (crimp) affect softness of handle of greasy wool and of wool textiles. In a replicated experiment, we investigated if wearers could detect the effect of using 17 µm superfine wool of low (74°/mm) or high (114°/mm) fiber curvature, and when the wools were blended with 17 µm cashmere (fiber curvature 49°/mm) in differing proportions, on four comfort sensations. Eight single jersey knitted fabrics were assessed under a controlled protocol using forearm sleeves made of the test fabric and a control fabric. Data (37 sensorial assessments of high curvature wool fabrics; 38 sensorial assessments of low curvature wool fabrics) were analyzed using linear mixed model analysis (restricted maximum likelihood), which included fixed effects for wool type and blend ratio and a random effect for participant. The use of a control sleeve fabric reduced variance due to participant effects by providing an anchor for each sensation over time. Wool fiber curvature affected participant assessment of breathability, comfort, feel after exercise (damp/dry) and skin feel (prickly/soft), with preferred values associated with high curvature (crimp) superfine wool. Increasing the proportion of cashmere in fabrics increased skin feel (better assessed softness). Skin feel was strongly associated with the evaluation of the fabrics by the Wool ComfortMeter and with increasing hairiness of yarns.

Predicting the pilling propensity of fabrics through artificial neural network modeling

Fabric pilling is affected by many interacting factors. This study uses artificial neural networks to model the multi-linear relationships between fiber, yarn and fabric properties and their effect on the pilling propensity of pure wool knitted fabrics. This tool shall enable the user to gauge the expected pilling performance of a fabric from a number of given inputs. It will also provide a means of improving current products by offering alternative material specification and/or selection. In addition to having the capability to predict pilling performance, the model will allow for clarification of major fiber, yarn and fabric attributes affecting fabric pilling.

Moisture transfer properties of bifacial fabrics

Many biological plants have bifacial leaves with an adaxial surface and an abaxial surface. These two surfaces can often have different morphologies and properties, and they serve different functions in plant growth. This has inspired us to develop novel bifacial fabrics, with a knitted structure on one face and a woven structure on the other. Bifacial fabrics were produced on a purpose-built machine, using wool, acrylic and polyester yarns, with the woven structure being plain weave, and the knitted structure being single jersey. In this study, the moisture properties of these fabrics were compared with conventional woven and knitted fabrics. The water contact angles of the bifacial fabrics were similar to knitted and woven fabrics, but the absorption time on the woven fabric was much higher than the other fabrics. Liquid moisture transfer properties on both faces of the bifacial fabrics were different, with water spreading and absorption on the woven face being quicker than on the knitted face. These unique properties of bifacial fabrics show that these fabrics could be used as moisture management fabrics, without the need for any additional treatments.

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.

An experimental study on fabric softness evaluation

Purpose – To examine a simple testing method of measuring the force to pull a fabric through a series of parallel pins to determine the fabric softness property.

Design/methodology/approach – A testing system was setup for fabric pulling force measurements and the testing parameters were experimentally determined. The specific pulling forces were compared with the fabric assurance by simple testing (FAST) parameters and subjective softness ranking. Their correlations were also statistically analyzed.

Findings – The fabric pulling force reflects the physical and surface properties of the fabrics measured by the FAST instrument and its ability to rank fabric softness appears to be close to the human hand response on fabric softness. The pulling force method can also distinguish the difference of fabrics knitted with different wool fiber contents.

Research limitations/implications – Only 21 woven and three knitted fabrics were used for this investigation. More fabrics with different structures and finishes may be evaluated before the testing method can be put in practice.

Practical implications – The testing method could be used for objective assessment of fabric softness.

Originality/value – The testing method reported in this paper is a new concept in fabric softness measurement. It can provide objective specifications for fabric softness, thus should be valuable to fabric community.

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.

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.

Relationship between wearer prickle response with fibre and garment properties and Wool ComfortMeter assessment

The prickle evoked by 48 knitted fabrics was assessed by wearers under a defined evaluation protocol. The relationship between the average wearer prickle score and known properties of constituent fibre, yarns and fabrics and fabric evaluation using the Wool ComfortMeter (WCM) was determined using linear modelling. After log transformation, the best model accounted for 87.7% of the variance. The major share of variation could be attributed to differences between mean fibre diameter (MFD) and WCM values. Low prickle scores were linearly associated with lower MFD, lower WCM and lower yarn linear density. There was an indication that yarn twist affected prickle scores and that fabrics composed of cotton evoked less prickle. Measures of fibre diameter distribution or coarse fibre incidence and other fabric properties were not significant. The analysis indicates that wool garments can be constructed to keep wearer assessed prickle to barely detectable levels and textile designers can manipulate a range of parameters to achieve similar wearer comfort responses.