Abstract:
Textile industry is contributing greatly to the country's economy by having
share of 55% in the total exports and by providing directly & indirectly
employment to about 35% of the total workforce of Pakistan. Weaving is
a major segment of textile industry contributing 7.5% to the global cotton
fabric exports. Woven fabrics are produced by the interlacements of warp
and weft yarns. These interlacements of yarns causes the yarns to follow
a wave like path. Therefore, extra length of yarns is required to get a
certain fabric length and width. This waviness of yarns in fabric is expressed
in terms of yarn crimp or fabric shrinkage. The amount of shrinkage or
crimp depends on the fabric structural parameters like yarn linear density,
yarn density in fabric (EPI, PPI),
oat length, GSM, reed count and reed
denting. The production cost along with physical and mechanical properties
of fabric vary with the amount of crimp percentage in warp and weft yarns.
Crimp percentage in yarns is analyzed by physically measuring the extra
yarn length or by predicting it on the basis of fabric structural parameters.
Previously developed methods of crimp analysis can be inaccurate, strenuous,
tedious, and involve high computation cost. These issues have been addressed
in the proposed framework that uses machine learning algorithms that are
random forests, KNN, kernel tricks, adaBoost and neural networks predicting
models. in the framework, the trained models were cross validated and
have a prediction accuracy (R2) of 0.86 and 0.79 for warp and weft yarn
crimp respectively. The nally trained models have also been evaluated on
unseen datasets that are from industry and related literature. The proposed
framework has prediction accuracy (R2) for warp & weft yarns crimp of 0.99
& 0.81 respectively for the industrial data set and of 0.99 for both warp and
weft crimp for the related work dataset. Presently the framework has been
trained only for fabrics made up of cotton carded yarns on air-jet loom. This
framework can be further enhanced for fabrics made of other type of yarns
and looms.
