Prediction of Residual Compressive Strength after Impact Based on Acoustic Emission Characteristic Parameters.

This study proposes a prediction method for residual compressive strength after impact based on the extreme gradient boosting model, focusing on composite laminates as the studied material system. Acoustic emission tests were conducted under controlled temperature and humidity conditions to collect characteristic parameters, establishing a mapping relationship between these parameters and residual compressive strength under small sample conditions. The model accurately predicted the residual compressive strength of the laminates after impact, with the coefficient of determination and root mean square error for the test set being 0.9910 and 2.9174, respectively. A comparison of the performance of the artificial neural network model and the extreme gradient boosting model shows that, in the case of small data volumes, the extreme gradient boosting model exhibits superior accuracy and robustness compared to the artificial neural network. Furthermore, the sensitivity of acoustic emission characteristic parameters is analyzed using the SHAP method, revealing that indicators such as peak amplitude, ring count, energy, and peak frequency significantly impact the prediction results of residual compressive strength. The machine-learning-based method for assessing the damage tolerance of composite laminates proposed in this paper utilizes the global monitoring advantages of acoustic emission technology to rapidly predict the residual compressive strength after the impact of composite laminates, providing a theoretical approach for online structural health monitoring of composite laminates. This method is applicable to various composite laminate structures under different impact conditions, demonstrating its broad applicability and reliability.