Predicting the seam efficiency of sewn blended fabrics using ANN and linear regression models
Abu Nassif, N. (2018). Predicting the seam efficiency of sewn blended fabrics using ANN and linear regression models. EKB Journal Management System, 8(1), 123-129. doi: 10.21608/idj.2018.86224
Najwa Ali Abu Nassif. "Predicting the seam efficiency of sewn blended fabrics using ANN and linear regression models". EKB Journal Management System, 8, 1, 2018, 123-129. doi: 10.21608/idj.2018.86224
Abu Nassif, N. (2018). 'Predicting the seam efficiency of sewn blended fabrics using ANN and linear regression models', EKB Journal Management System, 8(1), pp. 123-129. doi: 10.21608/idj.2018.86224
Abu Nassif, N. Predicting the seam efficiency of sewn blended fabrics using ANN and linear regression models. EKB Journal Management System, 2018; 8(1): 123-129. doi: 10.21608/idj.2018.86224

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

International Design Journal
Article 10, Volume 8, Issue 1 - Serial Number 25, January 2018, Page 123-129  XML PDF (220.6 K)
Document Type: Original Article
DOI: 10.21608/idj.2018.86224
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Author
Najwa Ali Abu Nassif
Fashion Design Department, Design and Art College, King Abdul Aziz University, Jeddah, kingdom of Saudi
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.      
Keywords
Seam Efficiency; Predicting; Fabric Properties; Artificial Neural Networks; Regression Analysis
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