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基于循环的机器学习模型,用于在不确定性下预测金属疲劳寿命
Xian-Ci Zhong1,2, Zhi-Yong Luo1,2, Ke-Shi Zhang1,2
1School of Civil Engineering and Architecture, Guangxi University, Nanning 530004, China.
Materials (Basel, Switzerland)
|September 27, 2025
概括
机器学习模型通过分析来自hysteresis循环的应力-应变数据来预测金属疲劳寿命. 这种方法量化了不确定性,并改善了在循环负荷下对Q235B等材料的预测.
科学领域:
- 材料科学 材料科学 材料科学
- 机械工程 机械工程
- 计算科学 计算科学
背景情况:
- 金属疲劳是工程元件的关键故障机制.
- 在不确定性下预测疲劳寿命仍然是一个重大挑战.
- 压力-应变歇斯底里循环包含有关疲劳过程中的材料行为有价值的信息.
研究的目的:
- 开发机器学习模型,用于在不确定性下预测金属疲劳寿命.
- 从歇斯底里循环中提取和利用应力-应变数据进行疲劳分析.
- 量化疲劳寿命预测中的不确定性.
主要方法:
- 在应变控制负载下分析来自Q235B的应力-应变歇斯底里斯环.
- 提取关键应力-应变数据点并将其转换为极坐标.
- 通过将参数扩展到间隔并生成随机数据来量化不确定性.
- 构建和优化三个机器学习模型:反向传播神经网络,支持向量回归和随机森林.
主要成果:
- 开发的机器学习模型为低周期疲劳寿命提供点和间隔预测.
- 这些模型证明了在不确定性下预测疲劳寿命的可行性和优势.
- 交叉验证被用于优化反向传播神经网络参数.
结论:
- 将机器学习模型与压力-应变歇斯底里循环分析相结合,为了解材料疲劳行为提供了一种强大的方法.
- 提出的方法有效地解决了小型疲劳测试数据集的挑战.
- 这项研究提供了关于用量化的不确定性预测金属疲劳寿命的见解.
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