The assimilation of simulated and measured data is essential for advancing technology in NDE 4.0. In this study, a particle filter (PF) was applied to identify the geometry of flaws for ultrasonic nondestructive testing. A PF based on a probabilistic approach that allows errors in measurement and simulation models may be of great assistance for the data assimilation. In the PF, state variables are expressed by random data samples called particles, together with their associated weights. The PF estimates the probabilistic density function of the state variables by merging simulation data with measured data. Data types must be physically identical in the simulation and measurement to enhance the accuracy and to accelerate the convergence speed in the PF. Here, the scattering component, which is specific information related to the flaw geometry, was used for the likelihood evaluation in the PF. The simulation, which needed many particles, was conducted using the elastodynamic finite integration technique accelerated by parallel computing with graphics processing units. The proposed PF approach was demonstrated in ultrasonic measurement, and the geometries of artificial flaws in aluminum specimens were identified using only one pulse-echo signal at a single transducer.

模拟和测量数据的同化对于NDE 4.0中的先进技术至关重要。在这项研究中，应用了粒子过滤器（PF）来识别缺陷的几何形状，以进行超声无损检测。基于概率方法的PF允许测量和仿真模型中的错误，这可能对数据同化有很大帮助。在PF中，状态变量由称为粒子的随机数据样本及其关联的权重表示。PF通过将模拟数据与测量数据合并来估计状态变量的概率密度函数。在仿真和测量中，数据类型在物理上必须相同，以提高准确性并加快PF中的收敛速度。在此，散射成分是与缺陷几何形状有关的特定信息，在PF中用于可能性评估。需要使用许多粒子的模拟是通过使用弹性动力有限积分技术进行的，该技术通过与图形处理单元的并行计算来加速。所提出的PF方法已在超声测量中得到了证明，并且在单个换能器上仅使用一个脉冲回波信号就可以识别出铝样品中的人工缺陷的几何形状。