Comparing AI methods for forecasting polyester fabric tensile property

Tensile properties of multifilament polyester woven fabrics are of great importance for their end uses such as parachutes, sails, tents, sleeping bags, filters and surgical textiles. The filament fineness, weave type and weave density have a great influence on the tensile properties of these fabrics...

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Bibliographic Details
Published in:	Neural computing & applications Vol. 36; no. 32; pp. 20561 - 20574
Main Authors:	Ayaz, Nurselin Özkan, Çelik, Halil İbrahim, Kaynak, Hatice Kübra
Format:	Journal Article
Language:	English
Published:	London Springer London 01-11-2024 Springer Nature B.V
Subjects:	Artificial Intelligence Artificial neural networks Computational Biology/Bioinformatics Computational Science and Engineering Computer Science Data Mining and Knowledge Discovery Density Design of experiments Elongation Error analysis Fineness Fuzzy logic Genetic algorithms Image Processing and Computer Vision Original Article Polyesters Probability and Statistics in Computer Science Regression coefficients Sails Tensile properties Weft Woven fabrics Fuzzy logic Woven fabric Artificial neural network Genetic algorithm
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Summary:	Tensile properties of multifilament polyester woven fabrics are of great importance for their end uses such as parachutes, sails, tents, sleeping bags, filters and surgical textiles. The filament fineness, weave type and weave density have a great influence on the tensile properties of these fabrics. In this study, artificial intelligence (AI) models such as artificial neural networks (ANN), fuzzy logic (FL) and genetic algorithms (GA) were developed to forecast breaking strength and breaking elongation values of multifilament polyester woven fabrics. The fabric samples used in the study have three different microfilament finenesses and two different conventional filament finenesses with plain, twill and satin weave types. By applying four different weft density values, totally 60 woven fabric samples were obtained in the experimental design. The regression coefficient values ( R 2 ) between actual and predicted results were obtained as 0.80, 0.90 and 0.92 with ANN, FL and ANN–GA hybrid methods, respectively. The mean absolute percentage error (MAPE) was lower than 6% for all AI techniques used in this study. As a conclusion, it was proved that the breaking strength and breaking elongation properties of multifilament polyester woven fabrics can be forecasted with high accuracy rates by AI techniques.
ISSN:	0941-0643 1433-3058
DOI:	10.1007/s00521-024-10284-1