通过增量基于信息的机器学习方法加快测量疲劳裂增长
Cheng Wen1,2, Haipeng Lu1, Yiliang Wang1,3
1School of Mechanical Engineering, Guangdong Ocean University, Zhanjiang 524088, China.
Materials (Basel, Switzerland)
|January 28, 2026
概括
这项研究引入了一种机器学习的插值-偏置策略 (MLIES),以加快疲劳测试. MLIES显著提高了裂增长预测的准确性,并减少了测试时间和成本.
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:
- 使用a-N曲线测量疲劳裂增长率是耗时和劳动密集的.
- 现有的方法通常需要大量的实验数据,从而限制了效率.
研究的目的:
- 开发一种机器学习的插值-抽取策略 (MLIES),以加快疲劳测试并提高预测准确性.
- 为了减少与疲劳裂生长实验相关的时间和成本.
主要方法:
- a-N 曲线的数据转换 (从 N 到 ΔN 和 a 到 Δa/ΔN) 以丰富数据量.
- 在早期数据上训练单层神经网络,使用指数级数据扩展和增强.
- 在合金和合金疲劳测试中验证MLIES方法.
主要成果:
- 与传统方法相比,MLIES可减少15-32%的测试时间/成本.
- 对a-N曲线实现了超过30%的更高预测准确度.
- 该模型直接从数据中学习了裂增长规则,没有明确的分析规律.
结论:
- MLIES提供了一种高效,数据驱动的方法,用于准确预测裂增长.
- 该战略提高了实验效率,使疲劳测试更快,更具成本效益.
- 这种方法对各种合金和实验条件有效.
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