Xiaohan Liu, Miao Tian, Yun Su, Yunyi Wang, Jun Li
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Predicting the Mechanical Strength of Fire Protective Fabrics after Thermal Aging using Machine Learning
Thermal aging leads to a reduction in the tensile strength of fire protective fabrics, which increases the skin burn risks of the wearer. Standardized test methods are generally destructive. In this study, machine learning was applied to predict the tensile strength after heat exposure. Training data was obtained from published articles, and seven features that affect the tensile strength of the fabric were determined. The results indicated that the average R2 and RMSE of machine learning models was 0.83 and 135.40, respectively, which was better than the traditional statistical model (R2 = 0.45, RMSE = 238.41). Among all the models, GBR produced the best prediction result (R2 = 0.95, RMSE = 77.42). Five features (fiber, weight, testing direction, exposure time, and heat flux density) were sufficient to achieve a better prediction.
期刊介绍:
AATCC Journal of Research. This textile research journal has a broad scope: from advanced materials, fibers, and textile and polymer chemistry, to color science, apparel design, and sustainability.
Now indexed by Science Citation Index Extended (SCIE) and discoverable in the Clarivate Analytics Web of Science Core Collection! The Journal’s impact factor is available in Journal Citation Reports.