铸造技术2025，Vol.46Issue(12)：1159-1176,18.DOI:10.16410/j.issn1000-8365.2025.5175
基于数据增强与机器学习的电子束熔化增材制造TiAl-4822合金疲劳寿命高精度预测研究
High-accuracy Fatigue Life Prediction of Electron Beam Melting Additively Manufactured TiAl-4822 Alloy Based on Data Augmentation and Machine Learning
摘要
Abstract
Ti-48Al-2Cr-2Nb(TiAl-4822)materials fabricated via electron beam melting(EBM)additive manufacturing exhibit pronounced variations in fatigue life under complex service conditions,which affects their engineering reliability.To this end,on the basis of fatigue test data consisting of 103 EBM-fabricated TiAl-4822 samples,a high-accuracy fatigue life prediction model(overall error＜20％)was developed by combining data augmentation techniques(SMOTE,SMOGN)with machine learning methods(hierarchical neural network,abbreviated as HNN,and Huber regression).The model first employs SMOTE to balance and argument the dataset and then integrates an HNN classifier to determine whether a sample would pass the fatigue test,achieving a classification accuracy of 80％.For the samples that failed the fatigue test,SMOGN was applied for data augmentation,and a two-stage model combining Huber regression with the HNN was used for fatigue life prediction,leading to an R2 of 0.81 and a mean absolute percentage error of 7.3％.SHAP analysis based on this model indicates that frequency,maximum stress,temperature,and stress amplitude are the primary influencing factors.A fatigue life prediction approach suitable for small-sample scenarios of EBM TiAl-4822 under service conditions is finally established.关键词
钛铝合金/电子束熔化/疲劳寿命/数据增强/机器学习/分层神经网络/稳健回归Key words
TiAl alloy/electron beam melting/fatigue life/data augmentation/machine learning/hierarchical neural network/Huber regression分类
矿业与冶金引用本文复制引用
叶嘉峰,林博超,鲍伊达,陈玮..基于数据增强与机器学习的电子束熔化增材制造TiAl-4822合金疲劳寿命高精度预测研究[J].铸造技术,2025,46(12):1159-1176,18.基金项目
国家重点研发计划(2021YFB3702602) （2021YFB3702602）
