缺陷参数化物理信息的神经网络用于激光超声波波场的前向和反向建模
Liu Yang1, Peipei Liu2, Kiyoon Yi1
1Department of Civil and Environmental Engineering, Korea Advanced Institute of Science and Technology (KAIST), Daejeon 34141, The Republic of Korea.
Ultrasonics
|January 7, 2026
概括
一个新的缺陷参数化物理信息神经网络 (DP-PINN) 准确地使用激光超声波特征金属元件的亚毫米表面缺陷. 这种非破坏性评估方法从有限的数据中重建完整的波场,确保结构完整性.
科学领域:
- 材料科学 材料科学 材料科学
- 机械工程 机械工程
- 人工智能的人工智能
背景情况:
- 非破坏性评估 (NDE) 对金属元件的完整性至关重要.
- 表面缺陷显著影响疲劳寿命和性能.
- 激光超声波提供非接触式检查,但在波场重建和从有限数据中识别缺陷方面面临挑战.
研究的目的:
- 开发一个基于物理学的神经网络 (PINN),用于描述金属元件的亚毫米表面缺陷.
- 为了实现激光超声波波场的准确前向和反向建模.
- 为了重建完整的波场并估计波速以进行缺陷分析.
主要方法:
- 提出了一个缺陷参数化的物理信息神经网络 (DP-PINN).
- 嵌入缺陷参数到弹性动力学方程中,用于波场重建.
- 模拟了四个缺陷案例,并分析了六个实践场景,数据稀疏度和先前知识不同.
主要成果:
- 通过仅使用0.42 MB的测量数据,实现了缺陷特征和完整的波场重建.
- 在不同类型的缺陷中展示了一致的缺陷检测能力.
- 获得了0.387的欧盟 (IoU) 上的平均交叉点,表明了定量准确性.
结论:
- 通过激光超声波,DP-PINN有效地通过激光超声波特征金属元件的亚毫米表面缺陷.
- 该方法可以在有限的数据中准确地重建波场和定位缺陷.
- 这种方法增强了确保结构完整性的非破坏性评估能力.
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