Accurately predicting fatigue life under multiaxial fatigue damage conditions is essential for ensuring the safety of critical components in service.However,due to the complexity of fatigue failure mechanisms,achievin...Accurately predicting fatigue life under multiaxial fatigue damage conditions is essential for ensuring the safety of critical components in service.However,due to the complexity of fatigue failure mechanisms,achieving accurate multiaxial fatigue life predictions remains challenging.Traditional multiaxial fatigue prediction models are often limited by specific material properties and loading conditions,making it difficult to maintain reliable life prediction results beyond these constraints.This paper presents a study on the impact of seven key feature quantities on multiaxial fatigue life,using Convolutional Neural Networks(CNN),Long Short-Term Memory Networks(LSTM),and Fully Connected Neural Networks(FCNN)within a deep learning framework.Fatigue test results from eight metal specimens were analyzed to identify these feature quantities,which were then extracted as critical time-series features.Using a CNN-LSTM network,these features were combined to form a feature matrix,which was subsequently input into an FCNN to predict metal fatigue life.A comparison of the fatigue life prediction results from the STFAN model with those from traditional prediction models—namely,the equivalent strain method,the maximum shear strain method,and the critical plane method—shows that the majority of predictions for the five metal materials and various loading conditions based on the STFAN model fall within an error band of 1.5 times.Additionally,all data points are within an error band of 2 times.These findings indicate that the STFAN model provides superior prediction accuracy compared to the traditional models,highlighting its broad applicability and high precision.展开更多
激光粉末床熔融(laser powder bed fusion,LPBF)成形悬垂结构的应力与变形是实现复杂金属构件高质量、高精度制造的关键问题之一。通过基体预埋应变片的方式实现了悬垂结构LPBF成形过程应变数据的实时测量。基于原位应变测量系统研究了...激光粉末床熔融(laser powder bed fusion,LPBF)成形悬垂结构的应力与变形是实现复杂金属构件高质量、高精度制造的关键问题之一。通过基体预埋应变片的方式实现了悬垂结构LPBF成形过程应变数据的实时测量。基于原位应变测量系统研究了T形悬垂结构、低角度悬垂结构(5°和10°)LPBF过程的原位应变行为。深入研究了不同悬臂长度、不同成形工艺参数对T形悬垂结构原位应变行为的影响,并进一步分析了不同悬垂角度、支撑类型对低角度悬垂结构原位应变行为的影响。结果表明,T形悬垂结构的悬空长度越长,结构的变形越大;采用激光能量梯度、棋盘扫描策略可有效降低T形悬垂结构的变形。支撑结构设计可显著影响低角度悬垂结构的应变行为和成形质量,采用H1支撑设计策略(块体支撑间距0.8 mm+锥体支撑间距0.6 mm)的成形质量最佳。上述结果可为深入理解LPBF成形悬垂结构的变形行为和调控提供有效参考。展开更多
基金supported by Key Program of National Natural Science Foundation of China(U2368215)the Science and Technology Research and Development Program Project of China Railway Group Co.,Ltd.(N2023J056).
文摘Accurately predicting fatigue life under multiaxial fatigue damage conditions is essential for ensuring the safety of critical components in service.However,due to the complexity of fatigue failure mechanisms,achieving accurate multiaxial fatigue life predictions remains challenging.Traditional multiaxial fatigue prediction models are often limited by specific material properties and loading conditions,making it difficult to maintain reliable life prediction results beyond these constraints.This paper presents a study on the impact of seven key feature quantities on multiaxial fatigue life,using Convolutional Neural Networks(CNN),Long Short-Term Memory Networks(LSTM),and Fully Connected Neural Networks(FCNN)within a deep learning framework.Fatigue test results from eight metal specimens were analyzed to identify these feature quantities,which were then extracted as critical time-series features.Using a CNN-LSTM network,these features were combined to form a feature matrix,which was subsequently input into an FCNN to predict metal fatigue life.A comparison of the fatigue life prediction results from the STFAN model with those from traditional prediction models—namely,the equivalent strain method,the maximum shear strain method,and the critical plane method—shows that the majority of predictions for the five metal materials and various loading conditions based on the STFAN model fall within an error band of 1.5 times.Additionally,all data points are within an error band of 2 times.These findings indicate that the STFAN model provides superior prediction accuracy compared to the traditional models,highlighting its broad applicability and high precision.
