基于机器学习的Al-Zn-Mg-Cu合金快速设计

Accelerated design of Al−Zn−Mg−Cu alloys via machine learning

提出一种基于机器学习的合金快速设计系统(ARDS),以定制所需性能的合金制备策略或预测制备策略所对应的合金性能。为此,分别对3种回归算法:线性回归(LR)、支持向量回归(SVR)和人工神经网络(BPNN)进行建模和比较以训练多性能预测模型。其中,应用SVR构建的机器学习模型被证明是最佳的。然后,基于生成对抗网络(GAN)模型原理,构建Al-Zn-Mg-Cu系铝合金快速设计系统(ARDS)。对ARDS的预测可靠性进行验证。结果表明,为了能够获得准确的制备策略,系统中极限抗拉强度(UTS)、屈服强度(YS)和伸长率(EL)的输入上限分别约为790 MPa、730 MPa和28%。此外,基于ARDS预测结果,制备了一种性能优异的新型铝合金材料,其UTS为764 MPa、YS为732 MPa、EL为10.1%,进一步验证了ARDS的可靠性。

A machine learning-based alloy rapid design system(ARDS)was proposed to customize the preparation strategies for the desired properties or predict the alloy properties following the preparation strategies.For achieving this,three regression algorithms:linear regression(LR),support vector regression(SVR),and back propagation neural network(BPNN),were employed separately to train the multi-property prediction model,in which the machine learning(ML)model built using SVR was proved to be the best.Then,inspired by the generative adversarial network(GAN)algorithm,the ARDS was constructed.The predictive reliability of ARDS was examined,and for the accurate prediction of the preparation strategies,the upper limits of ultimate tensile strength(UTS),yield strength(YS),and elongation(EL)are about 790 MPa,730 MPa,and 28%,respectively.Moreover,an ARDS-designed aluminum alloy with superior mechanical properties(764 MPa for UTS,732 MPa for YS,and 10.1%for EL)was experimentally fabricated,further verifying the reliability of ARDS.