摘要
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。
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 training.Compared with traditional model,whose parameters are obtained from conventional heuristic,the improved model has higher average correlation coefficient and lower average error.Therefore,it can predict the mechanical properties of AZ31 magnesium alloy on different annealed conditions more accurately.
出处
《重庆大学学报（自然科学版）》
EI
CAS
CSCD
北大核心
2011年第3期44-49,共6页
Journal of Chongqing University
基金
国家973计划资助项目(2007CB613704)
国家杰出青年基金资助项目(50725413)
关键词
镁合金
力学性能
人工神经网络
预测模型
全排列组合训练
magnesium alloys
mechanical properties
artificial neural network
prediction model
all permutations and combinations training
