In response to climate change,there has been a focus on developing lightweight and environmentally friendly materials,with active research aimed at enhancing the energy efficiency of electric and hybrid vehicles.In th...In response to climate change,there has been a focus on developing lightweight and environmentally friendly materials,with active research aimed at enhancing the energy efficiency of electric and hybrid vehicles.In this context,the development of polymer composites with superior thermal conductivity(TC)has been recognized as critical to meeting mechanical property requirements.This paper presents a machine learning model that utilized 1774 experimental data points to predict various properties of polymer composites,such as density,heat deflection temperature,flexural modulus,flexural strength,tensile yield strength,impact strength,and TC.Various data representation methods for composition data are employed,and the XGBoost model is trained,achieving high accuracy with an average R2 score of 0.95.This machine learning model,informed by experimental data,is a useful tool for predicting and optimizing the properties of polymer composites.展开更多
基金supported by the Ministry of Trade,Industry and Energy(MOTIE),Korea Institute for Advancement of Technology(KIAT)through the Virtual Engineering Platform Program(P0022334)Korea Planning and Evaluation Institute of Industrial Technology(KEIT)R&D Program(No.2000391).
文摘In response to climate change,there has been a focus on developing lightweight and environmentally friendly materials,with active research aimed at enhancing the energy efficiency of electric and hybrid vehicles.In this context,the development of polymer composites with superior thermal conductivity(TC)has been recognized as critical to meeting mechanical property requirements.This paper presents a machine learning model that utilized 1774 experimental data points to predict various properties of polymer composites,such as density,heat deflection temperature,flexural modulus,flexural strength,tensile yield strength,impact strength,and TC.Various data representation methods for composition data are employed,and the XGBoost model is trained,achieving high accuracy with an average R2 score of 0.95.This machine learning model,informed by experimental data,is a useful tool for predicting and optimizing the properties of polymer composites.
