The present work investigates the relationship between fatigue crack growth rate (da/dN) and stress intensity factor range (∆K) using machine learning models with the experimental fatigue crack growth rate (FCGR) data of cryo-rolled Al 2014 alloy. Various machine learning techniques developed recently provide a flexible and adaptable approach to explain the complex mathematical relations especially, non-linear functions. In the present work, three machine algorithms such as extreme learning machine (ELM), back propagation neural networks (BPNN) and curve fitting model are implemented to analyse FCGR of Al alloys. After tuning of networks with varying hidden layers and number of neurons, the trained models found to fit well to the tested data. The three tested models are compared with each other over the training as well as testing phase. The mean square error for predicting the FCG of cryo-rolled Al 2014 alloy by BPNN, ELM and curve fitting methods are 1.89, 1.84 and 0.09 respectively. While the ELM models outperform the rest of models in terms of training time, curve fitting model showed best performance in terms of accuracy over testing data with least mean square error (MSE). In terms of local optimisation, back propagation neural networks excel the other two models.

中文翻译：

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机器学习预测超细晶粒Al 2014合金的疲劳裂纹扩展行为

本工作研究了疲劳裂纹扩展速率（d a / d N）与应力强度因子范围（∆ K）之间的关系。），并使用机器学习模型和低温轧制的Al 2014合金的实验疲劳裂纹扩展率（FCGR）数据。最近开发的各种机器学习技术提供了一种灵活且适应性强的方法来解释复杂的数学关系，尤其是非线性函数。在本工作中，实现了三种机器算法，例如极限学习机（ELM），反向传播神经网络（BPNN）和曲线拟合模型，以分析铝合金的FCGR。在调整了具有不同隐藏层和神经元数量的网络之后，经过训练的模型发现非常适合测试数据。在训练以及测试阶段，将这三个测试模型进行相互比较。通过BPNN，ELM和曲线拟合方法预测冷轧Al 2014合金的FCG的均方误差为1.89、1.84和0。分别是09。尽管ELM模型在训练时间上胜过其他模型，但曲线拟合模型在准确性方面优于测试数据，且均方误差（MSE）最低，表现出最佳性能。在局部优化方面，反向传播神经网络优于其他两个模型。