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基于机器学习的点接关节的疲劳寿命预测方法的研究
Shanshan Li1, Zhenfei Zhan1, Jie Zou1,2
1School of Mechanical and Electrical and Vehicle Engineering, Chongqing Jiaotong University, Chongqing 400074, China.
Materials (Basel, Switzerland)
|August 14, 2025
概括
层显著减少了点关节疲劳寿命. 与传统方法相比,机器学习模型,特别是随机森林,在预测疲劳寿命方面提供了更高的准确性.
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:
- 点接接头对于工业结构至关重要.
- 评估它们的疲劳寿命对于安全性和可靠性至关重要.
- 化对疲劳寿命的影响需要进一步研究.
研究的目的:
- 开发一种用于预测点接接头的疲劳寿命的新方法.
- 为了评估层对疲劳寿命的影响.
- 将机器学习算法的预测准确度与传统方法进行比较.
主要方法:
- 对Q&P980钢点接接头进行了系统的疲劳测试.
- 应用传统的结构应力方法来预测疲劳寿命.
- 利用机器学习算法:随机森林,人工神经网络和高斯过程回归.
主要成果:
- 发现层显著降低了点接接头的疲劳寿命.
- 机器学习算法显示出更高的预测准确性和概括能力.
- 随机森林实现了0.93的R2值,超过了传统方法.
结论:
- 综合结构应力和机器学习的混合方法为疲劳寿命评估提供了有效的工具.
- 机器学习显示了提高现点接关节疲劳寿命预测的巨大潜力.
- 层的存在会对关节的耐用性产生负面影响,需要在设计时仔细考虑.
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