Abstract An increment learning scheme based on fully-connected neural network is proposed to predict the fatigue crack growth in middle tension, M(T), specimens of 7B04 T6 aluminum and TA15 titanium alloy under constant amplitude stress. Usually, the measurement of fatigue crack growth rates is labor-intensive and time-consuming, and the dataset of fatigue crack growth is small. Neural networks with back-propagation algorithm are not good at training on small dataset. Here we design network inputs which employ multiple increment information to overcome this shortage. Given the first part data points of crack growth in a specimen, the trained network can predict the rest for both aluminum alloy and titanium alloy without any prior knowledge. The trained network learns the underlying rules in experimental data of crack growth. Our method shows superiority to conventional fitting formulas and common neural networks such as recurrent neural network and long short-term memory method. Our work demonstrates the capacity of neural network and provides an alternative method to predict fatigue crack growth.

中文翻译：

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基于神经网络的增量学习方案预测疲劳裂纹扩展

摘要 提出了一种基于全连接神经网络的增量学习方案，用于预测7B04 T6铝和TA15钛合金等幅应力下中拉疲劳裂纹扩展M(T)。通常，疲劳裂纹扩展速率的测量费时费力，疲劳裂纹扩展数据集较小。带有反向传播算法的神经网络不擅长在小数据集上进行训练。在这里，我们设计了使用多个增量信息来克服这种不足的网络输入。给定样本中裂纹扩展的第一部分数据点，经过训练的网络可以在没有任何先验知识的情况下预测铝合金和钛合金的其余部分。经过训练的网络学习裂纹扩展实验数据中的基本规则。我们的方法显示出优于传统的拟合公式和常见的神经网络，如循环神经网络和长短期记忆方法。我们的工作证明了神经网络的能力，并提供了一种预测疲劳裂纹扩展的替代方法。