Flame retardant properties of metal hydroxide-based polymer composites: A machine learning approach
نویسندگان
چکیده
With increasing the practical applications of polymeric materials, surging demands for high-performance flame-retardant polymer composites are obvious. Accurately predicting performance materials is critical expediting technology development. Here, we develop machine learning models based on Random Forest algorithm to create standard paradigms a self-generated comprehensive dataset consisting 219 experimental data. Integrated by plenty randomly combined decision trees, well known its accurate predictability and easy interpretability. We predict important flame-retardancy-evaluating parameters including time ignition (TTI), peak heat release rate (pHRR), total (THR) flame retardancy index (FRI) different containing metal hydroxides as main fire-retardant additive. Our optimized achieve high accuracy with R-squared value 0.81 regression over 0.85 classification; mean absolute error below 0.3 all 4 models. The analysis features reveals that mass fraction MH takes always first place, influence feature groups target properties usually multi-dimensional complicated.
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ژورنال
عنوان ژورنال: Composites Communications
سال: 2023
ISSN: ['2452-2139']
DOI: https://doi.org/10.1016/j.coco.2023.101593