疲劳寿命预测:使用LSTM与上下文注意力模型预测金属材料的疲劳寿命
Hongchul Shin1,2, Taeyoung Yoon2, Sungmin Yoon2
1Department of Mechanical Engineering, Korea University Seoul 02841 Republic of Korea.
RSC advances
|May 14, 2025
概括
这项研究引入了结合LSTM和CNN的深度学习模型,以使用初始菌株数据准确地预测低周期疲劳 (LCF) 的寿命. LSTM-上下文注意力模型实现了卓越的性能,突出了其在LCF应用中的潜力.
科学领域:
- 材料科学 材料科学 材料科学
- 机械工程 机械工程
- 数据科学数据科学数据科学
背景情况:
- 低周期疲劳 (LCF) 数据呈现复杂的时间相互作用,使精确的疲劳寿命预测复杂化.
- 现有的方法很难通过仅使用初始周期数据来预测疲劳寿命,同时捕捉时间依赖和局部特征.
研究的目的:
- 开发一种新的深度学习模型,使用LCF数据准确预测疲劳寿命.
- 为了有效地捕捉时间依赖性和局部特征在压力-应变数据.
主要方法:
- 一个集长短期记忆 (LSTM) 和卷积神经网络 (CNN) 架构的深度学习模型.
- 整合注意力机制,以关注压力-应变数据中的关键时间步骤.
- 应变控制的疲劳测试,以获取LCF数据用于模型训练和验证.
主要成果:
- LSTM-上下文注意模型实现了高的确定系数 (R2 = 0.99),表现优于基线LSTM和CNN模型.
- 与传统方法相比,该模型显示出优越的统计指标.
- 注意重量分析证实了该模型能够识别与疲劳损伤相关的关键时间步骤的能力.
结论:
- 深度学习方法,特别是LSTM-上下文注意力模型,显示出在LCF应用中准确预测疲劳寿命的巨大潜力.
- 该模型能够从LCF数据中学习关键特征,为未来的研究提供了基础.
- 这种方法可以扩展到各种材料和复杂的疲劳机制.
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