Zhang Wei, Bao Zhangmin, Jiang Shan, He Jingjing
School of Reliability and Systems Engineering, Beihang University, Haidian District, Beijing 100089, China.
Materials (Basel). 2016 Jun 17;9(6):483. doi: 10.3390/ma9060483.
In the aerospace and aviation sectors, the damage tolerance concept has been applied widely so that the modeling analysis of fatigue crack growth has become more and more significant. Since the process of crack propagation is highly nonlinear and determined by many factors, such as applied stress, plastic zone in the crack tip, length of the crack, , it is difficult to build up a general and flexible explicit function to accurately quantify this complicated relationship. Fortunately, the artificial neural network (ANN) is considered a powerful tool for establishing the nonlinear multivariate projection which shows potential in handling the fatigue crack problem. In this paper, a novel fatigue crack calculation algorithm based on a radial basis function (RBF)-ANN is proposed to study this relationship from the experimental data. In addition, a parameter called the equivalent stress intensity factor is also employed as training data to account for loading interaction effects. The testing data is then placed under constant amplitude loading with different stress ratios or overloads used for model validation. Moreover, the Forman and Wheeler equations are also adopted to compare with our proposed algorithm. The current investigation shows that the ANN-based approach can deliver a better agreement with the experimental data than the other two models, which supports that the RBF-ANN has nontrivial advantages in handling the fatigue crack growth problem. Furthermore, it implies that the proposed algorithm is possibly a sophisticated and promising method to compute fatigue crack growth in terms of loading interaction effects.
在航空航天领域，损伤容限概念已得到广泛应用，因此疲劳裂纹扩展的建模分析变得越来越重要。由于裂纹扩展过程具有高度非线性，且受多种因素影响，如外加应力、裂纹尖端塑性区、裂纹长度等，因此难以建立一个通用且灵活的显式函数来准确量化这种复杂关系。幸运的是，人工神经网络（ANN）被认为是建立非线性多元投影的有力工具，在处理疲劳裂纹问题方面显示出潜力。本文提出了一种基于径向基函数（RBF）-ANN的新型疲劳裂纹计算算法，以从实验数据中研究这种关系。此外，还采用了一个名为等效应力强度因子的参数作为训练数据，以考虑载荷相互作用效应。然后将测试数据置于具有不同应力比或过载的等幅载荷下进行模型验证。此外，还采用了福尔曼方程和惠勒方程与我们提出的算法进行比较。当前的研究表明，基于ANN的方法与实验数据的吻合度比其他两种模型更好，这支持了RBF-ANN在处理疲劳裂纹扩展问题方面具有显著优势。此外，这意味着所提出的算法可能是一种用于计算考虑载荷相互作用效应的疲劳裂纹扩展的复杂且有前景的方法。
