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支持机器学习的多尺度建模平台,用于在压电复合结构中的损伤感应数字双胞胎.

Somnath Ghosh1, Saikat Dan2, Preetam Tarafder2

  • 1Civil & Systems Engineering, Johns Hopkins University, Baltimore, MD, 21218, USA. sghosh20@jhu.edu.

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概括

本研究介绍了一种新的数字双胞胎 (DT),用于实时检测压电复合材料的损坏. 先进的DT集成了微观细节和机器学习,使用电信号预测结构损坏.

关键词:
这就是ConvLSTM.电子机械损坏合器多尺度的PUCCDM模型压电复合材料 压电复合材料代表性的聚合微结构参数 (RAMP)

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科学领域:

  • 材料科学与工程 材料科学与工程
  • 机械工程 机械工程
  • 航空航天工程 航空航天工程

背景情况:

  • 非破坏性评估 (NDE) 对航空航天结构至关重要,但往往缺乏实时损害预测.
  • 现有的NDE方法通常是死后的,无法在现场捕捉不断变化的损伤.
  • 由于恶劣的操作条件,压电复合结构需要先进的监控.

研究的目的:

  • 开发用于压电复合结构的损伤感应数字双胞胎 (DT).
  • 为了实现实时,现场预测结构损坏的进展.
  • 将微观形态和机制纳入结构损坏评估中.

主要方法:

  • 一个两步的计算过程,结合了多尺度多物理建模和机器学习 (ML).
  • 开发一个参数上调的合构成性损伤力学 (PUCCDM) 模型.
  • 利用人工神经网络 (ANN) 和卷积长期短期记忆 (ConvLSTM) 网络来预测电信号的损害.

主要成果:

  • 通过PUCCDM模型,DT成功地将微观结构细节集成到宏观构成关系中.
  • 根据微观结构参数 (RAMPs) 的 ANN 衍生的 PUCCDM 系数.
  • ConvLSTM学习了电信号,损伤场和微观结构特征之间的相关性,从而能够准确地预测损伤.

结论:

  • 拟议的损害感应数字双胞胎提供实时,现场预测能力,用于在压电复合材料中演变的损害.
  • 该框架有效地使用有限的表面电信号测量来预测特定位置的损伤.
  • 这种方法提高了航空航天结构的运行安全和维护.