Wang Hongxun, Zhang Weifang, Sun Fuqiang, Zhang Wei
School of Reliability and Systems Engineering, Beihang University, Haidian District, Beijing 100191, China.
Materials (Basel). 2017 May 18;10(5):543. doi: 10.3390/ma10050543.
The relationships between the fatigue crack growth rate ( d a / d N ) and stress intensity factor range ( Δ K ) are not always linear even in the Paris region. The stress ratio effects on fatigue crack growth rate are diverse in different materials. However, most existing fatigue crack growth models cannot handle these nonlinearities appropriately. The machine learning method provides a flexible approach to the modeling of fatigue crack growth because of its excellent nonlinear approximation and multivariable learning ability. In this paper, a fatigue crack growth calculation method is proposed based on three different machine learning algorithms (MLAs): extreme learning machine (ELM), radial basis function network (RBFN) and genetic algorithms optimized back propagation network (GABP). The MLA based method is validated using testing data of different materials. The three MLAs are compared with each other as well as the classical two-parameter model ( K * approach). The results show that the predictions of MLAs are superior to those of K * approach in accuracy and effectiveness, and the ELM based algorithms show overall the best agreement with the experimental data out of the three MLAs, for its global optimization and extrapolation ability.
即使在巴黎区域，疲劳裂纹扩展速率（da/dN）与应力强度因子范围（ΔK）之间的关系也并非总是线性的。应力比对疲劳裂纹扩展速率的影响在不同材料中各不相同。然而，大多数现有的疲劳裂纹扩展模型无法妥善处理这些非线性问题。机器学习方法凭借其出色的非线性逼近和多变量学习能力，为疲劳裂纹扩展建模提供了一种灵活的方法。本文基于三种不同的机器学习算法（MLA）：极限学习机（ELM）、径向基函数网络（RBFN）和遗传算法优化的反向传播网络（GABP），提出了一种疲劳裂纹扩展计算方法。基于MLA的方法通过不同材料的测试数据进行了验证。将这三种MLA相互比较，同时也与经典的双参数模型（K方法）进行了比较。结果表明，MLA的预测在准确性和有效性方面优于K方法，并且基于ELM的算法在这三种MLA中总体上与实验数据的一致性最佳，因其具有全局优化和外推能力。
