用声辐射和基于机器学习的方法对哈德菲尔德钢的疲劳损伤的表征
Shengrun Shi1,2, Dengzun Yao3, Guiyi Wu1,2
1Centre of Excellence for Advanced Materials, Dongguan 523808, China.
Sensors (Basel, Switzerland)
|January 11, 2024
概括
本研究使用声辐射 (AE) 和机器学习来监测哈德菲尔德钢的疲劳裂增长. 机器学习有效地对疲劳子阶段进行分类,而不需要先前加载历史.
科学领域:
- 材料科学 材料科学 材料科学
- 机械工程 机械工程
- 非破坏性测试 不破坏性测试
背景情况:
- 结构健康监测 (SHM) 对设备安全至关重要.
- 声波发射 (AE) 是一种实时的SHM技术,用于疲劳裂生长 (FCG).
- 描述哈德菲尔德钢等材料的疲劳损伤演变需要先进的方法.
研究的目的:
- 使用AE和机器学习来描述哈德菲尔德钢的疲劳损伤演变.
- 为了将AE信号与疲劳损伤进展相关联.
- 开发一种基于机器学习的方法来分类疲劳子阶段.
主要方法:
- 在整个疲劳加载过程中获取和分析AE信号.
- 使用波形特征和双光谱分析对AE源机制的研究.
- 应用多个机器学习算法来对疲劳分阶段进行分类.
主要成果:
- AE信号与哈德菲尔德钢的疲劳损伤演变成功相关.
- 通过信号分析阐明了AE源机制.
- 机器学习算法在分类疲劳子阶段方面表现出高效.
- 拟议的方法绕过了先前AE-loading历史记录和∆K计算的需要.
结论:
- 整合AE和机器学习为SHM提供了一种新有效的方法.
- 机器学习对疲劳子阶段的分类比传统方法提供了显著的进步.
- 这项研究增强了工程结构中疲劳裂增长的实时监测能力.
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