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86224

Predicting the seam efficiency of sewn blended fabrics using ANN and linear regression models

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Last updated: 04 Jan 2025

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Abstract

In most cases, quality of sewn apparel products is characterized by seam performance. The durability of the seam is mainly defined by its efficiency along the seam line; therefore it is one of the most important characteristics to obtain the desired seam quality. Throughout this study, seam efficiencies of woven blended fabrics were predicted using two different methodologies, i.e. ANN and regression methods.  ANN with four neurons input layer, 15 neuron hidden layer and output layer with one neuron focusing on the seam efficiency was used and compared to regression line. The input variables in both predictive modes were polyester ratios, sewing needle size, stitch density and sewing thread count. The findings of this work revealed that ANN predictive model is outperformed the multiple linear regression one with lower vales of RMSE and MBE and high R2 values.      

DOI

10.21608/idj.2018.86224

Keywords

Seam Efficiency, predicting, Fabric Properties, Artificial Neural Networks, Regression Analysis

Authors

First Name

Najwa

Last Name

Abu Nassif

MiddleName

Ali

Affiliation

Fashion Design Department, Design and Art College, King Abdul Aziz University, Jeddah, kingdom of Saudi

Email

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City

Jeddah

Orcid

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Volume

8

Article Issue

1

Related Issue

12190

Issue Date

2018-01-01

Receive Date

2017-11-15

Publish Date

2018-01-01

Page Start

123

Page End

129

Print ISSN

2090-9632

Online ISSN

2090-9640

Article File

IDJ_Volume 8_Issue 1_Pages 123-129.pdf

PDF . 225.9kB

Link

https://idj.journals.ekb.eg/article_86224.html

Detail API

https://idj.journals.ekb.eg/service?article_code=86224

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10

Type

Original Article

Type Code

1,217

Publication Type

Journal

Publication Title

International Design Journal

Publication Link

https://idj.journals.ekb.eg/

MainTitle

Predicting the seam efficiency of sewn blended fabrics using ANN and linear regression models

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Type

Article

Created At

22 Jan 2023