Changes in fibre curvature during the processing of wool and alpaca fibres and their blends

This paper studied the wool and alpaca fibre curvature and its variation during the fibre processing. It revealed the effect of wool fibre crimp on the cohesion properties of alpaca and wool blended slivers. Different wool and alpaca tops were blended via a number of gillings, and the role of wool fibre curvature in alpaca/wool blend processing has also been investigated. During the wool fibre processing, fibre curvature tended to diminish gradually from scoured fibre to top. Blending wool with alpaca fibres improved the cohesion properties of the blended sliver, compared with pure alpaca slivers. For a high ratio of alpaca component in the blend, a high-crimp wool should be used to achieve good sliver cohesion.<br /

Investigation of fibre tension and fibre breakage in siumulated fibre opening processes

To reveal the mechanism of fibre damage and breakage in the fibre opening processes, the fibre tension during the interaction between a fibre and a pinned beater has been investigated. Details of the interacting force variations and incident of fibre breakage have been closely examined. Many factors which influence the fibre/pin interacting force have been elucidated. The results highlight the causes of fibre damage and breakage by fibre/pin interactions.<br /

Measuring the influence of fibre-to-fabric properties on the pilling of wool fabrics

This study ranks the contribution of various fibre, yarn and fabric attributes to the pilling of wool knitwear. On the basis of an artificial neural network modelling, a combination of sensitivity analysis, forwards/backwards search and genetic algorithms was used to identify the importance of various fibre/yarn/fabric input parameters. The three different techniques show broad similarities in their assessment of which input parameters are important or are not important in affecting fabric pilling. The ranking shows that fabric cover factor has the most effect on pilling, followed by yarn count and thin places, fibre length, yarn twist, etc. It is further illustrated that the directional trend of the predicted pilling outputs for a selection of inputs was in line with the expected behaviour. To verify the findings of input feature selection, input factors deemed to have a small effect on the predicted pilling output, such as fibre length and diameter variations and curvature, were removed and the subsequent performance statistically compared to the original multi-layer perceptron. Differences between the outputs predicted by the original and pruned models are found not to be statistically significant at the 5% significance level. Results from this study may help manufacturers and knitwear designers in choosing the most appropriate materials and structures to reduce the pilling propensity of wool knitwear. <br /

Mill specific prediction of worsted yarn performance

Different spinning mills use different raw materials, processing methodologies, and equipment, all of which influence the quality of the yarns produced. Because of many variables, there is a difficulty in developing a universal empirical/theoretical model. This work presents a multilayer perceptron algorithm (MLP) model for the purpose of building a mill specific worsted spinning performance prediction tool. Sixteen inputs are used to predict key yarn properties and spinning performance, including number of fibers in cross-section, unevenness (U%), thin places, neps, yarn tenacity, elongation at break, thick places, and spinning ends-down. Validation of the model on mill specific commercial data set shows that the general fit to the target values is good. Importantly, the performance of the MLP shows a certain degree of stability to different, random selections of independent test data. Subsequent comparison against the predicted outputs of Sirolan Yarnspec&trade; confirms the overall performance of the artificial neural network (ANN) method to be more accuratefor mill specific predictions. <br /

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. <br /