基于声发射和XGBoost的陶瓷基复合材料剩余强度预测

Residual strength prediction of ceramic matrix composites based on acoustic emission and XGBoost

为有效评估陶瓷基复合材料在载荷作用下的损伤状态,开展了陶瓷基复合材料加载过程中的声发射监测试验。对比了包括XGBoost、AdaBoost、KNN回归等多种机器学习算法在材料剩余强度预测方面的性能。通过基于SHAP的模型可解释性,分析了各相关特征对模型预测能力的贡献。结果表明:除关键声发射信号特征之外,基于声发射信号的计算特征——预警函数,对剩余强度模型预测能力的提升有较大帮助,基于XGBoost算法和声发射信号能够实现较精确的陶瓷基复合材料剩余强度预测。

To effectively evaluate the damage state of ceramic matrix composites under loading,acoustic emission(AE)monitoring experiments of ceramic matrix composites during the loading process was carried out.The performance of various machine learning algorithms including XGBoost,AdaBoost,and KNR for the prediction of material residual strength was compared.Finally,the contribution of each relevant feature to the model prediction ability was analyzed by SHAP-based model interpretability.The results showed that,in addition to the key AE features,the computational feature(Sentry function)based on the AE features contributed more to the improvement of the residual strength model prediction capability.Residual strength prediction of ceramic matrix composites was realized based on XGBoost algorithm and AE signals.