ANN Model for Annealing Parameter and Hardness of Zirconium Base Alloy

作者：Yu Junhui^*; Li Xiaoning; Zhai Yan; Ren Yan; Li Yan

来源：Rare Metal Materials and Engineering, 2012, 41(8): 1346-1350.

• zirconium base alloy
• ANN
• annealing parameter
• hardness

Back-propagation artificial neural network (BP ANN) module with Trainrp algorithm was used to predict annealing parameter and hardness of HANA-4(Zr-1.5Nb-0.4Sn-0.2Fe-0.1Cr)and HANA-6(Zr-1.1Nb-0.05Cu). In this module, chemical compositions, annealing temperature and annealing time were employed as input units while hardness was employed as output unit. The optimal network architecture was considered to be 3-7-1 with momentum factor and learning rate all chosen to be 0.6, respectively. The result of ANN module shows that the biggest relative error (RE) is 7%, and the correlation coefficients (R-values) is 0.98534. The result strongly indicates that the ANN model is an efficient tool to provide annealing parameter. Moreover, Sensitivity analysis (SA) shows that annealing temperature and annealing time are the most important parameters to the ANN model accuracy, the effect of chemical compositions is very small.

• 出版日期2012-8