The growing integration of artificial intelligence (AI) in ultrasonic guided-wave nondestructive testing (NDT) necessitates large-scale, high-fidelity simulation data for training scattering databases. To address this need, this paper proposed efficient and accurate forward and inverse approaches tailored for guided-wave applications. For forward analysis, a modified boundary element method (BEM) is developed, which incorporates far-field displacement patterns to effectively eliminate spurious reflections caused by model truncation compared to traditional BEM. This forward method preserves the dimension reduction advantage of conventional BEM and requires no absorbing layers, and accuracy rate reaches 100% nearly compared with theoretical Green’s functions. For inverse analysis, a linear reconstruction method based on Born approximation was proposed, which can directly reconstruct the specific shape of defects. Moreover, this inverse method does not need iteration, is both simple in formulation and straightforward to implement, and the accuracy rate of defect width and depth reconstruction can reach 100% by multi-incidence direction reconstruction. The validity and effectiveness of both proposed methods are rigorously demonstrated through multiple benchmark numerical simulations.
扫码关注我们
求助内容:
应助结果提醒方式:
