Tehnički vjesnik, Vol. 28 No. 1, 2021.
Izvorni znanstveni članak
https://doi.org/10.17559/TV-20191018180716
Machine Learning Approach for Prediction of Crimp in Cotton Woven Fabrics
Muhammad Zohaib Fazal
; School of Electrical Engineering and Computer Science (SEECS), National University of Sciences and Technology (NUST), Islamabad, Pakistan
Sharifullah Khan*
; School of Electrical Engineering and Computer Science (SEECS), National University of Sciences and Technology (NUST), Islamabad, Pakistan
Muhammad Azeem Abbas
; School of Electrical Engineering and Computer Science (SEECS), National University of Sciences and Technology (NUST), Islamabad, Pakistan
Yasir Nawab
; University Institute of InformationTechnology, Pir Mehr Ali Shah Arid Agriculture University Rawalpindi, Rawalpindi, Pakistan
Shahzad Younis
; National Textile University, Faisalabad, Pakistan
Sažetak
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 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 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.
Ključne riječi
cotton woven fabric; crimp prediction; machine learning; pre-production prediction
Hrčak ID:
251392
URI
Datum izdavanja:
5.2.2021.
Posjeta: 1.058 *