基于参数优化的人工神经网络的AZ31镁合金力学性能预测模型

通过力学性能试验测定了不同退火条件下AZ31镁合金的抗拉强度、屈服强度和延伸率,并利用人工神经网络技术建立了对应力学性能的预测模型,其中对模型的优化采用了一种新方法,即参数全排列组合训练.结果表明,基于全排列训练得到的最优参数建立的网络模型具有优良的性能,比经传统试探法构建的模型具有更高的平均相关系数和更低的平均误差,因此能更准确地预测AZ31镁合金在不同退火条件后的力学性能.

Mechanical property prediction model of AZ31 magnesium alloys based on an artificial neural network with parameter optimization

The tensile strength,yield strength and elongation of AZ31 magnesium alloys on different annealed conditions are tested by mechanical properties experiments.A model of corresponding mechanical properties is built by applying artificial neural network,and it is optimized by a new method,namely all permutations and combinations training of parameters.The results show that the network model has an excellent performance,which is based on optimal parameters obtained from all permutations and combinations trainin...