用声波发射和机器学习对热CFRP中的纤维断裂事件进行分类和集群
Richard Dela Amevorku1, David Amoateng-Mensah1, Manoj Rijal1
1Department of Mechanical Engineering, North Carolina A & T State University, 1601 E. Market Street, Greensboro, NC 27411, USA.
Sensors (Basel, Switzerland)
|October 29, 2025
概括
机器学习准确地使用声辐射信号对碳纤维增强聚合物 (CFRP) 复合材料的损伤进行分类. 这种结构健康监测方法达到99%的准确性,可以早期检测纤维断裂等关键故障模式.
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:
- 碳纤维增强聚合物 (CFRP) 复合材料在许多行业中至关重要.
- 在不同的负载条件下,CFRP会经历不同的损坏机制.
- 有效监测CFRP的完整性对于安全性和性能至关重要.
研究的目的:
- 为了研究在拉伸负荷下对热准同otropic CFRPs 的故障模式.
- 应用机器学习 (ML) 来使用声辐射 (AE) 信号对损坏进行分类.
- 评估AE参数和特征提取损坏分类的可靠性.
主要方法:
- 使用结构健康监测 (SHM) 方法与AE信号采集.
- 采用ML和集体学习算法进行实时AE信号分类.
- 分析了传统的AE参数和波形特征,用于损坏识别.
主要成果:
- 在分类故障模式 (纤维断裂,矩阵裂,分层) 中达到高达99%的准确性.
- 在分析大量AE数据时证明了ML的效率和可靠性.
- 使用交叉关联技术识别了复合材料故障附近的纤维断裂事件.
结论:
- 与SHM集成的ML可在CFRP中提供准确有效的损坏检测.
- 这种方法可以早期识别关键故障模式.
- 该研究强调了监测复合材料剩余寿命的潜力.
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