从声辐射信号检测金属的变形机制,通过知识驱动的无监督学习
Boyuan Gou1, Yan Chen1, Songhua Xu2
1State Key Laboratory for Mechanical Behavior of Materials, Xi'an Jiaotong University, Xi'an, China.
Nature communications
|July 27, 2025
概括
本研究引入了一种新的无监督学习方法,用于使用声辐射 (AE) 信号实时监测材料变形机制. 该方法准确地识别了多种同时存在的变形类型,从而实现了动态的早期故障警告.
科学领域:
- 材料科学 材料科学 材料科学
- 机械工程 机械工程
- 数据科学数据科学数据科学
背景情况:
- 及时检测材料变形对于防止结构故障至关重要.
- 声波发射 (AE) 是一种非破坏性的技术,用于监测材料的行为.
- 对传统方法来说,将多个同时存在的变形机制与AE信号区分开来仍然是一个挑战.
研究的目的:
- 开发一种新的知识驱动的无监督学习方法,用于识别AE信号中的共存变形机制.
- 为了提高材料损坏检测的准确性和实时监控能力.
- 为金属结构材料提供动态的早期故障警告.
主要方法:
- 一个以知识为导向的无监督学习框架,集成渐变驱动的监督基础学习者.
- 一个总损失函数注入领域知识,以实现无监督学习.
- 在拉力负载下使用多孔的316L不钢进行实验验证.
主要成果:
- 拟议的方法成功地识别了同时存在的脱位和裂纹AE信号.
- 识别信号的雪崩统计与经典的统计方法和断裂理论保持一致.
- 证明了强大的可转移性,用于识别新材料中的变形机制.
结论:
- 开发的无监督学习方法有效地从AE信号中区分了多个同时存在的变形机制.
- 与雪崩理论的整合允许实时监控和动态早期故障警告.
- 该框架为先进的材料健康监测提供了强大且可转移的解决方案.
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