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研究了Sr含量对Mg-Al-Si系镁合金中Mg-6Al-0.7Si-1Zn合金力学性能的影响,采用BP神经网络法建立了Mg-6Al-0.7Si-1Zn-xSr合金组织与力学性能的关系模型。采用BP神经网络预测的该合金力学性能与实验值接近,相对误差较小,最大误差为4.896%,最小误差仅为0.271%。结果表明,该模型具有很好的预测精度和较快收的敛速度,此模型的建立为研究Mg-Al-Si系镁合金提供了参考。
The effect of Sr content on the mechanical properties of Mg-6Al-0.7Si-1Zn alloy in Mg-Al-Si magnesium alloy was studied. The microstructure and mechanical properties of Mg-6Al-0.7Si-1Zn-xSr alloy were established by BP neural network Relationship model. The mechanical properties of the alloy predicted by BP neural network are close to the experimental values, the relative error is small, the maximum error is 4.896% and the minimum error is only 0.271%. The results show that this model has good prediction accuracy and faster convergence rate. The establishment of this model provides a reference for the study of Mg-Al-Si magnesium alloy.