多变量信号处理用于细混合层材料中故障模式的特征化,使用声波发射传感器
Sakineh Fotouhi1, Maher Assaad2, Mohamed Nasor2
1School of Engineering, University of Glasgow, Glasgow G12 8QQ, UK.
Sensors (Basel, Switzerland)
|June 10, 2023
概括
这项研究将混合复合材料的故障模式与声辐射 (AE) 信号相关联. 高振幅,能量和持续时间的AE事件表明碳层碎片化,挑战了关于高频信号的先前假设.
科学领域:
- 材料科学 材料科学 材料科学
- 机械工程 机械工程
- 复合材料 复合材料 复合材料
背景情况:
- 混合复合板材具有伪柔性行为,模仿柔性金属.
- 了解这些材料的故障机制对于结构完整性至关重要.
- 薄层复合材料在拉伸负荷下呈现独特的故障模式.
研究的目的:
- 为了将特定的故障模式与薄层伪柔性混合复合板材中的声辐射 (AE) 事件相关联.
- 调查AE信号特征与碎片化和分层化等故障机制之间的关系.
- 用先进的集群技术分析AE数据,以区分故障模式.
主要方法:
- 研究的单向 (UD) 和准同位素 (QI) 混合层材与S玻璃和碳预制材料.
- 应用单轴拉伸负荷来诱导故障模式.
- 采用多变量聚类方法,使用高斯混合模型来分析AE信号.
- 与观察到的故障模式相关的AE信号特征 (振幅,能量,持续时间,频率).
主要成果:
- 确定了两个不同的AE集群,对应于碳层碎片化和分层化.
- 高幅度,能量和持续时间的AE信号与碎片化有关,而不是通常认为的高频率.
- 纤维断裂和分层的序列可以通过多变量AE分析来识别.
- 发现故障模式的定量评估取决于各种因素,包括堆叠序列和材料特性.
结论:
- 声辐射分析可以有效地区分混合复合材料中的碎片化和分层化.
- 高强度的AE信号 (振幅,能量,持续时间) 是碳层碎片化的主要指标.
- 该研究挑战了高频AE信号与碳纤维碎片化之间存在直接相关性的假设.
- 需要进一步的研究来定量评估故障模式,因为它们对多种因素的复杂依赖.
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