Linear Model Equation for Prediction and Evaluation of Surface Roughness of Plain-Woven Fabric

Kura Alemayehu Beyene

Makale detayları

Linear Model Equation for Prediction and Evaluation of Surface Roughness of Plain-Woven Fabric

Yazarlar:

Kura Alemayehu Beyene

Yayıncı:

Ege University

Ana Başlıklar:

Wearable Materials

Dergi:

Textile and Apparel Vol.33 Issue 1, pp.88-94

Belge Tipi:

Makale

Yayın Tarihi:

3/2023

Belge Dili:

İngilizce

Document ID (Org No.):

2001286830

pISSN:

1300-3356

Anahtar kelimeler:

Özet:

 Nowadays, evaluating fabric touch can be a great interest of industries to match the quality needs of consumers and parameters for the manufacturing process. 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 estimate and evaluates without the complexity and time-consuming experimental procedures. In this research paper, the linear regression model was developed that was utilized for the prediction and evaluation of surface roughness of plain-woven fabric. The model was developed based on nine different half-bleached plain-woven fabrics with three weft Yarn counts 42 tex, 29.5 tex  14.76 tex, and three weft thread densities 18 picks per c, 21ppc  24 picks per c and then the surface roughness of plain-woven fabric was tested by using Kawabata KES-FB4 testing instrument. The findings reveal that the effects of count and density on the roughness of plain-woven fabric were found statistically significant at the confidence interval of 95. The weft yarn count has a positive correlation with surface roughness values of plain-woven fabrics. On the other hand, pick density has a negative correlation with the surface roughness values of plain-woven fabrics. The correlation between measured surface roughness by KES-FB4 and calculated surface roughness by the model equation show how they are strongly correlated at 95 R² of 0.97.

 Nowadays, evaluating fabric touch can be a great interest of industries to match the quality needs of consumers and parameters for the manufacturing process. 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 estimate and evaluates without the complexity and time-consuming experimental procedures. In this research paper, the linear regression model was developed that was utilized for the prediction and evaluation of surface roughness of plain-woven fabric. The model was developed based on nine different half-bleached plain-woven fabrics with three weft Yarn counts 42 tex, 29.5 tex  14.76 tex, and three weft thread densities 18 picks per c, 21ppc  24 picks per c and then the surface roughness of plain-woven fabric was tested by using Kawabata KES-FB4 testing instrument. The findings reveal that the effects of count and density on the roughness of plain-woven fabric were found statistically significant at the confidence interval of 95. The weft yarn count has a positive correlation with surface roughness values of plain-woven fabrics. On the other hand, pick density has a negative correlation with the surface roughness values of plain-woven fabrics. The correlation between measured surface roughness by KES-FB4 and calculated surface roughness by the model equation show how they are strongly correlated at 95 R² of 0.97.

 Nowadays, evaluating fabric touch can be a great interest of industries to match the quality needs of consumers and parameters for the manufacturing process. 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 estimate and evaluates without the complexity and time-consuming experimental procedures. In this research paper, the linear regression model was developed that was utilized for the prediction and evaluation of surface roughness of plain-woven fabric. The model was developed based on nine different half-bleached plain-woven fabrics with three weft Yarn counts 42 tex, 29.5 tex  14.76 tex, and three weft thread densities 18 picks per c, 21ppc  24 picks per c and then the surface roughness of plain-woven fabric was tested by using Kawabata KES-FB4 testing instrument. The findings reveal that the effects of count and density on the roughness of plain-woven fabric were found statistically significant at the confidence interval of 95. The weft yarn count has a positive correlation with surface roughness values of plain-woven fabrics. On the other hand, pick density has a negative correlation with the surface roughness values of plain-woven fabrics. The correlation between measured surface roughness by KES-FB4 and calculated surface roughness by the model equation show how they are strongly correlated at 95 R² of 0.97.

 Nowadays, evaluating fabric touch can be a great interest of industries to match the quality needs of consumers and parameters for the manufacturing process. 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 estimate and evaluates without the complexity and time-consuming experimental procedures. In this research paper, the linear regression model was developed that was utilized for the prediction and evaluation of surface roughness of plain-woven fabric. The model was developed based on nine different half-bleached plain-woven fabrics with three weft Yarn counts 42 tex, 29.5 tex  14.76 tex, and three weft thread densities 18 picks per c, 21ppc  24 picks per c and then the surface roughness of plain-woven fabric was tested by using Kawabata KES-FB4 testing instrument. The findings reveal that the effects of count and density on the roughness of plain-woven fabric were found statistically significant at the confidence interval of 95. The weft yarn count has a positive correlation with surface roughness values of plain-woven fabrics. On the other hand, pick density has a negative correlation with the surface roughness values of plain-woven fabrics. The correlation between measured surface roughness by KES-FB4 and calculated surface roughness by the model equation show how they are strongly correlated at 95 R² of 0.97.

Makale detayları

Linear Model Equation for Prediction and Evaluation of Surface Roughness of Plain-Woven Fabric

Yazarlar:

Kura Alemayehu Beyene

Yayıncı:

Ege University

Ana Başlıklar:

Wearable Materials

Dergi:

Textile and Apparel Vol.33 Issue 1, pp.88-94

Belge Tipi:

Makale

Yayın Tarihi:

3/2023

Belge Dili:

İngilizce

Document ID (Org No.):

2001286830

pISSN:

1300-3356

Surface Roughness

Regression Modeling

Design Expert

Prediction

Plain Woven Fabric

and Structural Parameters

Sayı

Makale detayları

Linear Model Equation for Prediction and Evaluation of Surface Roughness of Plain-Woven Fabric

Yazarlar: Kura Alemayehu Beyene

Yayıncı: Ege University

Ana Başlıklar: Wearable Materials

Dergi: Textile and Apparel Vol.33 Issue 1, pp.88-94

Belge Tipi: Makale

Yayın Tarihi: 3/2023

Belge Dili: İngilizce

Document ID (Org No.): 2001286830

pISSN: 1300-3356

Surface Roughness

Regression Modeling

Design Expert

Prediction

Plain Woven Fabric

and Structural Parameters

Sayı

Yazarlar:

Kura Alemayehu Beyene

Yayıncı:

Ege University

Ana Başlıklar:

Wearable Materials

Dergi:

Textile and Apparel Vol.33 Issue 1, pp.88-94

Belge Tipi:

Makale

Yayın Tarihi:

3/2023

Belge Dili:

İngilizce

Document ID (Org No.):

2001286830

pISSN:

1300-3356