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Modeling helps to determine how structural parameters of fabric affect the surface of a fabric and also identify the way they influence fabric properties. Moreover, it helps to estimate and evaluate without the complexity and time-consuming experimental procedures. The purpose of this study is to develop and select the best regression model equations for the prediction and evaluation of surface roughness of plain-woven fabrics.

In this study, a linear and quadratic regression model was developed for the prediction and evaluation of surface roughness of plain-woven fabrics, and the capability in accuracy and reliability of the two-model equation was determined by the root mean square error (RMSE). The Design-Expert AE11 software was used for developing the two model equations and analysis of variance “ANOVA.” The count and density were used for developing linear model equation one “SMD1” as well as for quadratic model equation two “SMD2.”

From results and findings, the effects of count and density and their interactions on the roughness of plain-woven fabric were found statistically significant for both linear and quadratic models at a confidence interval of 95%. The count has a positive correlation with surface roughness, while density has a negative correlation. The correlations revealed that models were strongly correlated at a confidence interval of 95% with adjusted R² of 0.8483 and R² of 0.9079, respectively. The RMSE values of the quadratic model equation and linear model equation were 0.1596 and 0.0747, respectively.

Thus, the quadratic model equation has better capability accuracy and reliability in predictions and evaluations of surface roughness than a linear model. These models can be used to select a suitable fabric for various end applications, and it was also used for tests and predicts surface roughness of plain-woven fabrics. The regression model helps to reduce the gap between the subjective and objective surface roughness measurement methods.

The purpose of this study is to investigate the effects of weft yarn diameter and pick density on the properties of surface roughness (SMD) of 3/1 (Z) twill-woven fabrics in three measurement directions weft (0°), the warp (90°) and the diagonal (45°).

Nine 3/1 (Z) twill samples were prepared with two factors and three levels and their roughness values were measured in the weft (0°), warp (90°) and diagonal (45°) directions of 3/1 (Z) twill fabrics using the Kawabata-FB4 instrument. Analysis of variance (ANOVA) is used to determine the effect of weft yarn diameter and pick density on SMD properties and comparisons were done in the weft (0°), the warp (90°) and the diagonal (45°) directions.

From experimental analysis, weft yarn diameter and pick density affect SMD of 3/1 (Z) twill-woven fabrics in both diagonal (45°) and weft (0°) directions but slightly affect warp (90°) direction. Maximum SMD values were observed in diagonal (45°) directions and the minimum was in warp (90°) directions of fabrics. Weft yarn diameter and pick density are statistically significant on SMD values of 3/1 (Z) twill-woven fabrics for three directions at a 95% confidence interval. Parameter variation in weft directions of 3/1 (Z) twill-woven fabrics also varies SMD values in three directions measurements

The findings of this study can be usually used for textile technology, industries and laboratories to create a basic understanding for measuring roughness properties of 3/1 (Z) twill fabric. It is also possible to identify the surface characterizations in different directions of measurement for their usage in some specific areas of end application like consumer goods, home textiles, technical textiles, etc.

Friction is considered to be one property of cloth that has considerable importance in the fields of both technological and subjective assessment for surface properties of textile fabrics. The purpose of this study is to investigate the affective aspects of yarn and fabric structural parameters on the behavior of surface friction of plain woven fabrics.

In this study, nine varieties of half-bleached cotton plain-woven fabrics with three weft yarn count (tex) and three weft thread density (ppc) are produced and will be examined for their frictional characteristics. The surface frictional properties of plain-woven fabrics were measured by using Kawabata (KES-Fb4) testing instrument. The ANOVA analysis is used to determine how yarn (count) and fabric (density) structural parameters does influence the surface friction properties of the fabrics. Also, the interaction effects between the factors (count and density) on the response variable (surface friction) of plain-woven fabrics.

The findings of this study revealed that the effects of weft yarn count and pick-density have statistically significant on the frictional behavior of the fabric surface properties at a 95% confidence interval. Thus, weft yarn count has a positive correlation with both coefficient of friction (MIU) and mean deviation of coefficient of friction (MMD) on frictional behavior of the fabric surface properties. On the other hand, pick density has a negative correlation with both MIU and MMD on frictional behavior of the fabric surface properties. The weft count, pick density and their interactions (Count X Density) have multicollinearity in the experiment term because the variance inflation factor values were greater than one.

The findings of this study can be routinely used across the textile industries and laboratories to provide a fundamental understanding regarding the surface frictional properties of the woven fabric for different end applications concerning the yarn structural parameters and fabric structural parameters. And the relationship of count and density with surface friction of plain woven fabrics.

This study aims to manufacture alternative window shutters using waste cotton fabrics by stiffening using polyvinyl acetate (PVA) with vinyl acrylic binder solutions.

The manufactured fabrics were evaluated for their tensile strength, drapeability, bending length by weight and color fastness to light. And finally, an analysis of variance was done for each parameter.

As the percent of PVA with a vinyl acrylic solution and the number of layers increased, the tensile strength, drape coefficient (percent), bending length (cm), and color fastness to light increased in both directions. The percent of PVA with a vinyl acrylic solution and the number of layers are statistically significant for each response such as tensile strength, drape coefficient (percent), bending length (cm), color fastness to light and water repellency at a 95% confidence interval. Tensile strength, drape coefficient (%) and bending length (cm) are always greater in the warp direction than in the weft direction. The tensile strength, drape coefficient (percent), bending length (cm) and color fastness to light of treated fabrics samples are greater than those of the untreated fabrics.

The factory waste fabrics can be recycled into window shutters which will provide the cheaper raw material for window shutter manufacturers.