改进的神经网络模型在变形镁合金发展中的应用

摘要

Neural network models of mechanical properties prediction for wrought magnesium alloys were improved by using more reasonable parameters, and were used to develop new types of magnesium alloys. The parameters were confirmed by comparing prediction errors and correlation coefficients of models, which have been built with all the parameters used commonly with training of all permutations and combinations. The application was focused on Mg-Zn-Mn and Mg-Zn-Y-Zr alloys. The prediction of mechanical properties of Mg-Zn-Mn alloys and the effects of mole ratios of Y to Zn on the strengths in Mg-Zn-Y-Zr alloys were investigated by using the improved models. The predicted results are good agreement with the experimental values. A high strength extruded Mg-Zn-Zr-Y alloy was also developed by the models. The applications of the models indicate that the improved models can be used to develop new types of wrought magnesium alloys.%采用更为合理的建模参数,将预测变形镁合金力学性能的神经网络模型进行改进,并将此模型用于发展新型镁合金;对所有建模参数以全排列组合训练的方式构建模型,并通过比较这些模型的预测误差及相关系数来确定最合理的建模参数.模型的应用主要有Mg-Zn-Mn和Mg-Zn-Y-Zr两种合金.运用改进后的模型对Mg-Zn-Mn合金的力学性能进行预测,研究Mg-Zn-Y-Zr合金中Y/Zn摩尔比对强度的影响.最后,还利用此模型发展了一种高强挤压态的Mg-Zn-Y-Zr合金.结果表明:模型预测值与实验值吻合较好,改进后的模型可以用于发展新型变形镁合金.