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基于机器学习的E36钢接接头的疲劳寿命预测
Lina Zhu1, Hongye Guo2, Zongxian Song3
1Wheel Rail Center, Tianjin Research Institute for Advanced Equipment, Tsinghua University, Tianjin 300300, China.
Materials (Basel, Switzerland)
|August 14, 2025
概括
机器学习模型准确地预测E36钢接接头的疲劳寿命,优于传统方法. 人工神经网络显示出最佳性能,增强了船舶建造的结构完整性预测.
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:
- E36钢对于造船和离岸结构至关重要,但在接接头上遭受疲劳故障.
- 接脚和受热影响区域的裂显著降低了E36钢件的疲劳寿命.
- 现有的E36钢接的疲劳寿命预测方法是复杂的,低效的,缺乏精度.
研究的目的:
- 开发一种高效的机器学习 (ML) 框架,用于预测E36钢接接头的疲劳寿命.
- 通过数据增强技术解决疲劳测试中的数据短缺问题.
- 将各种ML模型的性能与传统预测公式进行比较.
主要方法:
- 为E36接接头建立了23个疲劳测试数据点的数据集.
- 使用Z参数模型和SMOTE进行数据增强,以解决数据稀缺问题.
- 机器学习模型,包括人工神经网络和随机森林,在增强和原始数据集上进行训练和评估.
主要成果:
- 机器学习模型的表现明显优于传统的Z参数公式 (R2 = 0.643,MAPE = 16.15%).
- 人工神经网络获得了最高的准确性 (R2 = 0.972,MAPE = 4.45%).
- 随机森林模型在验证和测试集中显示出强大的一致性 (R2 ≈ 0.89).
结论:
- 拟议的ML框架为E36钢接接头的疲劳寿命预测提供了更准确,更有效的方法.
- 人工神经网络和随机森林模型显示,在增强结构部件疲劳寿命评估方面具有显著的前景.
- 这种方法可以提高使用E36钢制造的结构的可靠性和安全性.
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