Predicting the thermal protective performance of flame-retardant fabric based on machine learning
International Journal of Clothing Science and Technology
ISSN: 0955-6222
Article publication date: 12 February 2024
Issue publication date: 2 April 2024
Abstract
Purpose
The purpose of this study is to predict the thermal protective performance (TPP) of flame-retardant fabric more economically using machine learning and analyze the factors affecting the TPP using model visualization.
Design/methodology/approach
A total of 13 machine learning models were trained by collecting 414 datasets of typical flame-retardant fabric from current literature. The optimal performance model was used for feature importance ranking and correlation variable analysis through model visualization.
Findings
Five models with better performance were screened, all of which showed R2 greater than 0.96 and root mean squared error less than 3.0. Heat map results revealed that the TPP of fabrics differed significantly under different types of thermal exposure. The effect of fabric weight was more apparent in the flame or low thermal radiation environment. The increase in fabric weight, fabric thickness, air gap width and relative humidity of the air gap improved the TPP of the fabric.
Practical implications
The findings suggested that the visual analysis method of machine learning can intuitively understand the change trend and range of second-degree burn time under the influence of multiple variables. The established models can be used to predict the TPP of fabrics, providing a reference for researchers to carry out relevant research.
Originality/value
The findings of this study contribute directional insights for optimizing the structure of thermal protective clothing, and introduce innovative perspectives and methodologies for advancing heat transfer modeling in thermal protective clothing.
Keywords
Acknowledgements
The authors would like to acknowledge the support of the Fundamental Research Funds for the Central Universities (Grant No. 2232023D-06/2232024G-08) and the International Cooperation Fund of Science and Technology Commission of Shanghai Municipality (Grant No. 21130750100).
Citation
Li, B., Tian, M., Liu, X., Li, J., Su, Y. and Ni, J. (2024), "Predicting the thermal protective performance of flame-retardant fabric based on machine learning", International Journal of Clothing Science and Technology, Vol. 36 No. 2, pp. 226-240. https://doi.org/10.1108/IJCST-12-2022-0175
Publisher
:Emerald Publishing Limited
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