The interlacements of yarns in woven fabrics cause the yarn to follow a wavy path that produces crimp.Off-loom width of the fabric is determined by the percentage of the induced crimp.Therefore, the final width of the fabric will be less or surplus than required if crimp percentage is not precisely measured.Both excessive or recessive fabric width is unwanted and marvos t coils leads to huge loss of cost (profit), manufacturing time, energy (electricity) and ultimately loss of competition.Crimp percentage in yarns is determined by physically measuring the extra yarn length or by predicting it based on fabric structural parameters.
Existing methods are mainly post-production, time and resource intensive that require specialized skills and tangible fabric samples.The proposed framework applies supervised machine redken shades eq 07m driftwood learning for crimp prediction to cater for the limitations of the existing techniques.The framework has been cross-validated and has prediction accuracy (R2) of 0.86 and 0.79 for warp and weft yarn crimp respectively.
It has prediction accuracy (R2) for warp and weft yarns crimp of 0.99 and 0.81 respectively for the unseen industrial dataset.The proposed prediction model shows better performance when compared with an existing standard system.