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海水对复合材料三明治结构机械性能的影响:机器学习框架
Norman Osa-Uwagboe1,2, Amadi Gabriel Udu2,3, Vadim V Silberschmidt1
1Wolfson School of Mechanical, Electrical, and Manufacturing Engineering, Loughborough University, Loughborough LE11 3TU, UK.
Materials (Basel, Switzerland)
|June 19, 2024
概括
这项研究表明,声辐射和机器学习可以预测湿度暴露后海洋复合结构的损伤. 这些方法准确地识别了故障模式,提高了结构安全性和材料优化.
科学领域:
- 材料科学 材料科学 材料科学
- 机械工程 机械工程
- 海洋技术 海洋技术
背景情况:
- 纤维增强塑料三明治结构由于其有利的性能,在海上应用中至关重要.
- 评估湿度吸收对在外平面负荷下结构完整性的影响,对于安全和材料优化至关重要.
- 音响发射 (AE) 和机器学习 (ML) 等先进技术为损害评估和结构健康监测提供了潜力.
研究的目的:
- 为了研究复合材料三明治结构在吸收水分后的机械性能.
- 应用AE和ML来识别和预测各种负载条件下的损坏模式.
- 评估ML模型在评估海洋应用的结构完整性的有效性.
主要方法:
- 在E-玻璃纤维/聚乙烯化泡三明治结构上进行了准静态缩试验.
- 分析了时间和频率领域的声辐射 (AE) 特征.
- 使用K-means++集群来识别损坏模式.
- 集体学习算法开发了海水暴露和未暴露样本的损害预测模型.
主要成果:
- 对AE数据的分析揭示了与不同损伤模式相对应的独特模式.
- ML模型准确地分类了各种孔几何形状和负载条件的损伤模式.
- 预测准确度得分从86.4%到95.9%不等.
结论:
- 机器学习与声波排放相结合,显示出预测海洋复合结构损伤演变的巨大潜力.
- 这种方法可以加强结构健康监测和海洋应用的材料优化.
- 开发的模型有效地在现实的环境和负载条件下评估结构完整性.
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