在导电复合材料中通过深度学习方法识别载荷位置和幅度
Zhen-Hua Tang1, Di-Sen Hu1, Jun-Rong Pan1
1College of Aerospace Engineering, Chongqing University, Chongqing 400044, China.
Sensors (Basel, Switzerland)
|February 13, 2026
概括
本研究引入了用于复合结构健康监测的深度学习方法. 它使用简单的电极设置准确识别外部负荷的位置和力,改进了现有技术.
科学领域:
- 材料科学 材料科学 材料科学
- 电气工程 电气工程
- 人工智能的人工智能
背景情况:
- 目前用于复合结构健康监测 (SHM) 的电动自传感方法存在局限性.
- 其中包括复杂的电极安排和难以准确定位和量化外部负载的困难.
研究的目的:
- 开发基于深度学习的自我传感方法,用于准确的负载定位和振幅识别.
- 解决复合SHM现有方法的局限性.
主要方法:
- 用3D导电网络制备带电填充剂填充的树脂复合材料.
- 在复合板边缘安装四个电极,以收集边界电阻响应.
- 基于残留学习的卷积神经网络 (CNN) 模型的开发,用于负载分析.
主要成果:
- 经过训练的CNN模型准确地确定了施加机械负荷的位置和振幅.
- 实现了0.91mm的定位误差和0.13N的振幅误差.
- 与以前的研究相比,表现出优越的性能.
结论:
- 拟议的深度学习方法为预测负载位置和幅度提供了一个高度准确和高效的方法.
- 这种技术在复合结构健康监测中具有广泛应用的巨大潜力.
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