Artificial neural network and multiple linear regression modeling for predicting thermal transmittance of plain-woven cotton fabric

Author:

Akter Mahmuda1ORCID,Khalil Elias2,Uddin Md. Haris3,Chowdhury Md. Kamrul Hassan1,Hasan Shah Md. Maruf1

Affiliation:

1. Department of Apparel Engineering, Bangladesh University of Textiles, Bangladesh

2. Department of Textiles, Bangabandhu Textile Engineering College, Bangladesh

3. Department of Statistics, University of Dhaka, Bangladesh

Abstract

The present research compares a machine learning model with a statistical model, with specific emphasis on artificial neural networks and multiple linear regression models. The aim of this study is to forecast the thermal transmittance of a plain-woven cotton fabric using input data such as thread density measured in ends per inch, picks per inch, and fabric thickness. The artificial neural network is built using a network with feed-forward backpropagation, and the MATLAB software’s training function trainlm is used to modify its weight and basic values based on Levenberg–Marquardt optimization techniques. The sigmoid transfer function is used to set the layer output and measure network performance in terms of the root mean squared error, mean absolute error percentage, and coefficient of determination which were determined. For the artificial neural network prediction model, the root mean squared error and mean absolute error percentage were 1.05 and 3.132%, respectively, while the coefficient of determination was 0.9307. In contrast, the multiple linear regression prediction model had root mean squared error and mean absolute error percentage values of 2.98 and 8.97%, respectively, along with a coefficient of determination of 0.4727. The results reveal that the artificial neural network model outperforms the multiple linear regression model, showing superior accuracy and robustness in capturing the intricate interactions between important fabric parameters (ends per inch, picks per inch, and thickness) and thermal transmittance values. This research emphasizes the efficiency of artificial neural network modeling as a superior tool for forecasting thermal transmittance in textile applications rather than employing the time-consuming trial-and-error process for delivering significant insights for material engineering and energy-efficient design.

Publisher

SAGE Publications

Reference40 articles.

1. Fabric Selection for the Reference Clothing Destined for Ergonomics Test of Protective Clothing: Physiological Comfort Point of View

2. Chapter 3—Principles of thermal comfort

3. United Nations Economic and Social Commission for Western Asia. Thermal transmittance. 2023. https://archive.unescwa.org/thermal-transmittance (2023, accessed 19 September 2023).

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