基于BP神经网络的BSTMUF601高温合金蠕变本构模型

Creep deformation constitutive model of BSTMUF601 superalloy using the BP neural network method

摘要:

通过对BSTMUF601高温合金在1253 K和1368 K不同载荷下蠕变试验及基于恒应力条件的θ映射法蠕变本构模型,研究马弗炉真实服役环境下的蠕变行为。为了解决蠕变过程试件截面积减小的问题,提出一种直径修正法近似获得试件的真实应力和应变。考虑在恒载条件下,非线性多元拟合方法不能准确标定蠕变本构模型的参数,本文基于上述修正的应力和应变通过误差反向传播(BP)神经网络的学习算法逆向标定θ映射法模型参数。结果表明,预测结果与实验结果吻合良好,最大相对误差小于12 %。此外,模型计算的表观蠕变应力指数和TEM图像表明位错攀爬是蠕变变形主导机制,说明BP神经网络方法对BSTMUF601高温合金蠕变本构模型参数识别和预测方面的优势。

Abstract:

A series of creep tests of BSTMUF601 superalloy were carried out at different loads and temperatures to investigate creep behaviors at actual service environment. The diameter correction method was proposed to evaluate true stress and strain approximately for addressing the issue that the decrease of sectional area of specimens. And the θ projection creep constitutive model was used for characterizing creep deformation behaviors considering the advantage of reflecting the deformation process under constant true stress conditions. However, the parameters of creep constitutive model cannot be identified accurately by nonlinear multivariate fitting method under constant load conditions. In this paper, these constitutive parameters were calibrated by BP neural network importing temperature, time, stress and strain evaluated from the above correction method as inputs with back-propagation learning algorithm. Consequently, the calibrated constitutive model is determined, the predicted values coincide well with experimental results and the maximum relative error is less than 12%. Moreover, both the apparent creep stress exponent estimated by θ model, experimental results and the TEM patterns indicated the creep deformation mechanism may be dislocation climb, further indicating the BP neural network method is feasible for predicting complex models.