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通过声波发射和机器学习检测在单轴负荷下合金中的变形机制
Yan Chen1, Boyuan Gou1, Yihao Gao1
1State Key Laboratory For Mechanical Behavior of Materials, Xi'an Jiaotong University, Xi'an, China.
Advanced materials (Deerfield Beach, Fla.)
|February 18, 2026
概括
先进的机器学习 (ML) 与物理模型相结合,现在可以破译金属的复杂声辐射 (AE) 信号,改善健康监测. 这一突破有助于识别多种变形机制,对于预测材料故障至关重要.
科学领域:
- 材料科学 材料科学 材料科学
- 机械工程 机械工程
- 信号处理 信号处理
背景情况:
- 由于存在多个并存的变形机制,金属中的声辐射 (AE) 信号是复杂的.
- 由于信号叠加,对这些机制的可靠识别具有挑战性.
- 了解这些机制对于金属和合金的预测性健康监测至关重要.
研究的目的:
- 审查新兴的策略来解AE信号从共存的变形机制.
- 突出物理统计模型和机器学习 (ML) 之间的协同作用,用于机制识别.
- 评估这些方法在实时监测和故障预测中的应用.
主要方法:
- 建立特定的AE特征和潜在的变形过程之间的相关性.
- 批判性地评估ML驱动的战略,以区分变形机制.
- 审查实时监控和使用AE和ML的故障预测方面的进展.
主要成果:
- 物理统计模型和ML之间的协同作用为识别变形机制提供了新的途径.
- 建立了AE特征和在单轴负荷下变形过程之间的基本相关性.
- 机器学习技术显示出对实时监控和故障预测的前景.
结论:
- 物理模型和ML的整合是理解复杂AE信号的关键.
- 需要进一步的研究,以从实验室转移基于AE的机制识别到工程应用.
- 这种方法增强了对金属和合金的预测健康监测能力.
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