Development and comparison of artificial neural network and statistical model for prediction of thermo-physiological properties of polyester–cotton plated fabrics

Y Jhanji,D Gupta,V K Kothari

doi:10.1186/s40691-016-0071-z

Abstract

AbstractThermo-physiological properties of textiles play a very crucial role in providing thermal equilibrium to human beings in changing ambient conditions and activity level and in turn dictate the overall wearer comfort. A number of prediction tools like mechanistic, statistical and stochastic (artificial neural network) models are finding application in textiles for reasonable prediction of various aspects of textiles before the actual commencement of fabric production and testing. In this study, thermo-physiological properties of polyester–cotton plated fabrics were predicted by two approaches: artificial neural network and response surface equations. A multilayered back propagation artificial neural network was developed with four input nodes corresponding to four selected input parameters: back layer yarn linear density, filament fineness, total yarn linear density and loop length and one output node corresponding to the predicted thermo-physiological property. Four individual networks working in tandem with common set of input parameters and each giving an individual output were developed such that the outputs of four networks were thermal resistance, thermal absorptivity, air permeability and moisture vapour transmission rate respectively. Network architecture gave good prediction performance with low values of mean absolute percentage error and high coefficient of determination. Response surface equations were developed to predict the thermo-physiological properties and good agreement between experimental and predicted values for all the properties was found with coefficient of determination over 0.9. Artificial neural network predicted the thermal resistance and air permeability of plated fabrics with good accuracy. However, the response surface equations served better prediction tool for thermal absorptivity and moisture vapour transmission rate prediction.

Highlights

Prediction of functional and performance properties of textiles before the actual commencement of fabric production and testing can serve as an effective tool in characterization and designing of fabrics for any desired application
Mean absolute percentage error for thermal resistance, thermal absorptivity, air permeability and moisture vapour transmission rate were 2.03, 3.1, 3.15 and 2.58 % for training data set and 4.59, 5.13, 7.40 and 7.25 % respectively for test data set for individual networks to predict four properties individually
Comparison of artificial neural network (ANN) and response surface equations in terms of their prediction performance showed that both the approaches could explain over 90 % variability in the thermo-physiological properties (R2 value over 0.9)

Summary

Introduction

Prediction of functional and performance properties of textiles before the actual commencement of fabric production and testing can serve as an effective tool in characterization and designing of fabrics for any desired application. Artificial neural network is a stochastic (based on probabilistic method) and heuristic model (action based on prior experience) (Zurada 1997; Bhattacharjee 2007; Kothari and Bhattacharjee 2011). It simulates the functioning of a biological neuron and every component of the network is analogous to the actual constituents or operations of a biological neuron (Zurada 1997; Majumdar 2011a, 2011b).

Methods

Results

Conclusion