高强、高导铝合金研发的机器学习策略

Constructing a material research and design platform using machine learning frameworks to analyze and predict material properties has become an important tool for developing new materials. The electrical conductivity and ultimate tensile strength of aluminum alloys are often mutually exclusive,with an increase in electrical conductivity accompanied by a decrease in strength. Five machine learning methods, SVM, RF, ELM, BP and DNN, were used to develop machine learning prediction models for the electrical conductivity and tensile strength of 6000 series aluminum alloys. It is found that using thermodynamic data and processing processes as feature inputs shows great potential in the construction of alloy performance prediction models. And finally, a deep neural network prediction model with high accuracy and good generalization ability is proposed. After validation with experimental data, the reliability of this model for predicting the electrical conductivity and strength of aluminum alloys is demonstrated.