A Defect Localization Approach Based on Improved Areal Coordinates and Machine Learning

The defects are usually generated during the structural materials subjected to external loads. Elucidating the position distribution of defects using acoustic emission (AE) technique provides the basis for investigating the failure mechanism and prevention of materials and estimating the location of the potentially dangerous sources. However, the location accuracy is heavily affected by both limitation of localization area and reliance on the premeasured wave velocity. Here, we propose a novel AE source localization approach based on generalized areal coordinates and a machine learning algorithmic model. A total of 14641 AE source location simulation cases are carried out to validate the proposed method. The simulation results indicate that even under various measurement error conditions the AE sources could be effectively located. Moreover, the feasibility of the proposed approach is experimentally verified on the AE source localization system. The experiment results show that the mean localization error of 3.64 mm and the standard deviation of 2.61 mm are obtained, which are 67.55% and 75.46% higher than those of the traditional method.