Machine Learning for Clothing Performance Prediction
topic
Machine learning models trained on databases of fibre properties, fabric construction parameters, composite layering configurations, and measured TPP and THL performance values are applied to predict thermal and physiological performance of novel composite configurations without physical sample manufacturing and laboratory testing, accelerating the material and construction development process by identifying promising design directions from computational screening before committing to experimental validation.
Role
Accelerates structural firefighting clothing development through computational screening of design options that would require prohibitive physical testing if evaluated experimentally, with machine learning prediction enabling the exploration of much larger design spaces than experimental programmes can efficiently cover and identifying non-intuitive material combinations that outperform conventional designs in specific performance dimensions requiring targeted experimental validation.