用LSTM模型迁移学习预测铝7075合金高周疲劳性能
High Cycle S-N curve prediction for Al 7075-T6 alloy using Recurrent Neural Networks (RNNs)
- 基于拉伸疲劳数据训练LSTM，迁移预测扭转载荷下的S-N曲线
- 成功预测了远超传统测试范围的高循环寿命数据
- 适合材料疲劳测试成本高、样本少的研究场景
铝合金广泛使用，但易发生疲劳失效。表征材料疲劳性能耗时耗资，尤其是高周疲劳数据更难获取。为此，提出一种基于迁移学习的框架，利用长短期记忆网络（LSTM）对铝7075-T6合金的纯轴向疲劳数据进行训练，再迁移用于预测扭转载荷下的S-N曲线。该框架可准确预测更高循环次数下的扭转S-N曲线，有望显著降低材料疲劳特性获取成本，并在测试资源受限时优化实验优先级。
原文摘要 · Abstract (English)
Aluminum is a widely used alloy, which is susceptible to fatigue failure. Characterizing fatigue performance for materials is extremely time and cost demanding, especially for high cycle data. To help mitigate this, a transfer learning based framework has been developed using Long short-term memory networks (LSTMs) in which a source LSTM model is trained based on pure axial fatigue data for Aluminum 7075-T6 alloy which is then transferred to predict high cycle torsional S-N curves. The framework was able to accurately predict Al torsional S-N curves for a much higher cycle range. It is the belief that this framework will help to drastically mitigate the cost of gathering fatigue characteristics for different materials and help prioritize tests with better cost and time constraints.
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