A statistical prediction model for pilling grades of blended worsted fabrics based on fabric bending rigidity

Mustafa, Eman; Malek, Abdel Salam; Gholmy, Sherwet El; Geiheini, Adel El; kateb, Sherien El

doi:10.1016/j.aej.2022.05.022

Cited by 4 publications

(2 citation statements)

References 13 publications

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“…Table (7) shows that weft yarn count has the highest value of coefficient of correlation compared to picks per inch and fabric pattern. Table (8) shows that model ( 3) is an acceptable tool to predict fabric stiffness from proposed factors that affecting fabric pattern because all values of error are closed to (0) that indicate to a high performance of prediction model. Table (9) displays that model ( 3) is the best model of prediction performance by applying regression analysis to predict fabric stiffness.…”

Section: Modelmentioning

confidence: 99%

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Prediction of Fabric Stiffness Based On Fabric Construction

Mustafa,

Malek,

El Gholmy

et al. 2023

Industrial Technology Journal

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This research presents a statistical tool to predict fabric stiffness based on fabric construction to avoid material waste, effort and time consuming in laboratories testing. Three different blended material of fabric were used that made from wool and polyester with different percentages of material yarns, counts and type (single/ply) of weft yarn, and fabric weave pattern (plain, twill and satin). Quantitative and qualitative variables were used in prediction by applying regression analysis and Artificial Neural Network (ANN) with a suitable coding system for different variables. Three independent models were derived to predict fabric stiffness by using regression analysis and compared to ANN model. Model (3) is the best prediction tool depending on regression analysis to predict fabric stiffness due to higher interpretation of the relation between variables used in prediction of fabric stiffness. Whereas, ANN achieved higher performance of validation (MSE = 7.41E-05) and total value of (R 2 = 0.967) when comparing to regression analysis model.

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Section: Modelmentioning

confidence: 99%

“…Independent models of regression analysis were applied for every type of fabric weave structure. Mustafa E. [8] proposed a prediction tool for fabric pilling grades based regression analysis based on count of weft yarns, type of material and fabric structure. In addition, Çelik, H. İ.…”

Section: Introductionmentioning

confidence: 99%

Prediction of Fabric Stiffness Based On Fabric Construction

Mustafa,

Malek,

El Gholmy

et al. 2023

Industrial Technology Journal

View full text Add to dashboard Cite

show abstract

Mechanical response of composite materials prepared with polyurethane elastomers and polyvinyl chloride films

Liu

2023

Journal of the Mechanical Behavior of Biomedical Materials

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An experimental study of mechanical properties and comfortability of knitted imitation woven shirt fabrics

Zhao,

Cao,

2023

AUTEX Research Journal

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The weft knitted shirt fabric has good elasticity and comfortability, but it is difficult to achieve the formability of woven shirts. In this study, 30D XLANCE® elastic fibers and 30D Lycra® elastic fibers were used, which paired with 40S cotton and 75D/36F moisture-absorbing and quick-drying polyester DTY to develop the woven shirt fabrics on a 32 needle/25.4 mm single-sided circular machine. The results indicate that the 30D XLANCE® knitted shirt fabric has undergone sufficient heat setting treatment, and its mechanical properties are close to those of the standard woven fabric, superior to 30D Lycra® and 30D XLANCE® without sufficient heat setting treatment fabrics. The ultraviolet resistance behavior of XLANCE® fabrics is significantly better than that of Lycra® fabrics. Meanwhile, the XLANCE® fabrics after heat setting still maintain the excellent elasticity and resilience, breathability, moisture absorption, and quick drying performance, and the dimensional stability after washing is also very good. In addition, compared with spandex, XLANCE® fabrics have the lower heat setting temperature, which is beneficial for reducing energy consumption and carbon emissions. It can be considered that XLANCE® elastic fibers are the ideal raw materials for the knitted woven products.

show abstract

A statistical prediction model for pilling grades of blended worsted fabrics based on fabric bending rigidity

Cited by 4 publications

References 13 publications

Prediction of Fabric Stiffness Based On Fabric Construction

Prediction of Fabric Stiffness Based On Fabric Construction

Mechanical response of composite materials prepared with polyurethane elastomers and polyvinyl chloride films

An experimental study of mechanical properties and comfortability of knitted imitation woven shirt fabrics

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