Compression after multiple impact strength of composite laminates prediction method based on machine learning approach

Abstract

The intelligent structural health monitoring system that can evaluate the structural safety online is the future development trend, in which the strength online prediction is the key step. This study developed a machine learning (ML) method to predict the compression-after-impact (CAI) strength of carbon/glass hybrid laminates subjected to multiple impacts at different impact positions online, which can help to find and replace damaged materials quickly to prevent irreversible disasters caused by accidental impact. Firstly, a finite element model verified by experiments was established to obtain the data of training ML model. Secondly, the eXtreme Gradient Boosting (XGBoost) model was utilized to predict the CAI strength of the composites subjected to multiple impacts at different distances between impact positions (DBIP). In addition, the feature importance of impact parameters based on the SHapley Additive exPlanations (SHAP) method was also studied. The results showed that the prediction accuracy and efficiency of ML-based method were better than that of FEM. Impact energy was the most significant factor affecting CAI strength, and DBIP cannot be ignored. The proposed method has great potential in online structural integrity monitoring systems of high-performance composite structures.