This research presents a hybrid physics-aided multi-layer feed forward neural network (MLFFNN) model to improve damage detection under Lamb wave responses. Here, a damage parameter database (DPD) is created from the complex responses of a thin aluminum plate generated using finite-element (FE) simulations. A double pulse-echo transducer configuration is implemented over the 1.6 mm thick aluminum plate with notch-like defect, which generates only A\(_{0}\) mode in the plate structure and records damage-specific S\(_{0}\) mode. Sixty-six FE simulations are conducted, each representing a distinct damage scenario in terms of damage location and Lamb wave frequency. Artificial noise is added to compensate environmental interference. Orthogonal matching pursuit was performed to improve the sparsity of the signal. Thereafter, the damage-specific features are extracted from the sparsed S\(_{0}\) signal to construct DPD for all 66 FE simulations. The fully developed DPD is deployed to train an MLFFNN supervised by a robust Levenberg–Marquardt algorithm. A set of initial tests are conducted for higher damage-depth to plate-thickness ratio with 1.0 mm notch depth, and the fully trained MLFFNN predicts the damage location with 99.94% accuracy. The proposed algorithm achieves a good level of generalization, including the cases of overlapping echoes and cluttered responses due to multiple reflections for the given damage scenarios.

这项研究提出了一种混合的物理辅助多层前馈神经网络（MLFFNN）模型，以改进Lamb波响应下的损伤检测。在此，通过使用有限元（FE）模拟生成的铝薄板的复杂响应来创建损伤参数数据库（DPD）。双脉冲回波换能器配置在厚度为1.6 mm的具有缺口状缺陷的铝板上执行，该板在板结构中仅生成A \（_ {0} \）模式并记录特定于损伤的S \（_ {0 } \）模式。进行了六十六次有限元模拟，每个模拟都代表了不同的损坏场景，包括损坏位置和兰姆波频率。添加了人工噪声以补偿环境干扰。进行正交匹配追踪以改善信号的稀疏性。此后，从稀疏的S \（_ {0} \）中提取特定于损伤的特征信号以构建用于所有66个有限元仿真的DPD。完整开发的DPD被部署为训练由健壮的Levenberg-Marquardt算法监督的MLFFNN。进行了一系列初始测试，以得到更高的损伤深度与板厚之比，切口深度为1.0 mm，经过全面训练的MLFFNN以99.94％的准确度预测损伤的位置。所提出的算法达到了良好的泛化水平，包括在给定的损坏场景下，由于多次反射而产生的回波重叠和响应混乱的情况。