Mid Sweden University

Accurate localization of acoustic emission (AE) sources in composite materials remains a significant challenge due to the material’s anisotropy, complex wave propagation paths, and environmental noise. This study proposes a Long Short-Term Memory-optimized Time Difference Mapping method (LSTM-TDM) to address these challenges. By leveraging deep learning to analyze the time difference features of AE signal propagation, the proposed method significantly enhances the localization accuracy of the TDM method. Comparative experiments were conducted using three methods: the TDM method, the General Regression Neural Network optimized TDM method (GRNN-TDM), and the proposed LSTM-TDM method. The results demonstrate that the GRN-NTDM method improves localization accuracy by 15.38% compared to the TDM method, while the LSTM-TDM method achieves the best performance with an average error of 14.26 mm, a 50.63% reduction in error relative to the TDM method.The findings indicate that the LSTM-TDM method offers significant advantages in AE source localization for composite materials, providing critical insights and potential applications for structural health monitoring in such materials.