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Artificial neural network and regression models for prediction of sewing thread consumption for multilayered fabric assembly at lockstitch 301 seam

Md Vaseem Chavhan (Department of Textile Technology, Vignan’s Foundation for Science Technology and Research, Guntur, India)
M. Ramesh Naidu (Department of Chemical Engineering, Vignan’s Foundation for Science Technology and Research, Guntur, India)
Hayavadana Jamakhandi (Department of Textile Technology, Osmania University, Hyderabad, India)

Research Journal of Textile and Apparel

ISSN: 1560-6074

Article publication date: 17 August 2021

Issue publication date: 28 November 2022

59

Abstract

Purpose

This paper aims to propose the artificial neural network (ANN) and regression models for the estimation of the thread consumption at multilayered seam assembly stitched with lock stitch 301.

Design/methodology/approach

In the present study, the generalized regression and neural network models are developed by considering the fabric types: woven, nonwoven and multilayer combination thereof, with basic sewing parameters: sewing thread linear density, stitch density, needle count and fabric assembly thickness. The network with feed-forward backpropagation is considered to build the ANN, and the training function trainlm of MATLAB software is used to adjust weight and basic values according to the optimization of Levenberg Marquardt. The performance of networks measured in terms of the mean squared error and the layer output is set according to the sigmoid transfer function.

Findings

The proposed ANN and regression model are able to predict the thread consumption with more accuracy for multilayered seam assembly. The predictability of thread consumption from available geometrical models, regression models and industrial empirical techniques are compared with proposed linear regression, quadratic regression and neural network models. The proposed quadratic regression model showed a good correlation with practical thread consumption value and more accuracy in prediction with an overall 4.3% error, as compared to other techniques for given multilayer substrates. Further, the developed ANN network showed good accuracy in the prediction of thread consumption.

Originality/value

The estimation of thread consumed while stitching is the prerequisite of the garment industry for inventory management especially with the introduction of the costly high-performance sewing thread. In practice, different types of fabrics are stitched at multilayer combinations at different locations of the stitched product. The ANN and regression models are developed for multilayered seam assembly of woven and nonwoven fabric blend composition for better prediction of thread consumption.

Keywords

Citation

Chavhan, M.V., Naidu, M.R. and Jamakhandi, H. (2022), "Artificial neural network and regression models for prediction of sewing thread consumption for multilayered fabric assembly at lockstitch 301 seam", Research Journal of Textile and Apparel, Vol. 26 No. 4, pp. 343-358. https://doi.org/10.1108/RJTA-09-2020-0103

Publisher

:

Emerald Publishing Limited

Copyright © 2021, Emerald Publishing Limited

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