通过原位电化学充氢方法(电流密度0、2和4 m A/cm^(2))研究了45Cr Ni MoVA钢的低周疲劳行为及其断裂机制。结果表明:该材料在循环加载过程中呈现出应变幅值依赖的非饱和循环软化现象和non-Masing特性,其中non-Masing行为在低应变幅条件...通过原位电化学充氢方法(电流密度0、2和4 m A/cm^(2))研究了45Cr Ni MoVA钢的低周疲劳行为及其断裂机制。结果表明:该材料在循环加载过程中呈现出应变幅值依赖的非饱和循环软化现象和non-Masing特性,其中non-Masing行为在低应变幅条件下表现尤为显著。尽管充氢电流密度对材料的循环滞回行为无明显影响,但材料的抗疲劳性能却明显依赖于充氢电流密度大小和应变幅值。随着充氢电流密度的增加,材料内部氢浓度增高,导致疲劳损伤加速累积,且高应变幅工况下氢致寿命劣化效应显著高于低应变幅工况。扫描电镜(SEM)断口分析表明,充氢显著改变了材料的疲劳断裂机制:未充氢试样呈现典型的表面裂纹萌生与韧性断裂特征;而随充氢电流密度和应变幅值的提高,充氢试样的裂纹萌生位置由试样表面向内部缺陷转移,且脆性特征(准解理与沿晶分离形态)显著增强。充氢试样裂纹萌生区与扩展区均呈现韧窝、准解理和沿晶分离形态并存的混合断裂特征。展开更多
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
文摘通过原位电化学充氢方法(电流密度0、2和4 m A/cm^(2))研究了45Cr Ni MoVA钢的低周疲劳行为及其断裂机制。结果表明:该材料在循环加载过程中呈现出应变幅值依赖的非饱和循环软化现象和non-Masing特性,其中non-Masing行为在低应变幅条件下表现尤为显著。尽管充氢电流密度对材料的循环滞回行为无明显影响,但材料的抗疲劳性能却明显依赖于充氢电流密度大小和应变幅值。随着充氢电流密度的增加,材料内部氢浓度增高,导致疲劳损伤加速累积,且高应变幅工况下氢致寿命劣化效应显著高于低应变幅工况。扫描电镜(SEM)断口分析表明,充氢显著改变了材料的疲劳断裂机制:未充氢试样呈现典型的表面裂纹萌生与韧性断裂特征;而随充氢电流密度和应变幅值的提高,充氢试样的裂纹萌生位置由试样表面向内部缺陷转移,且脆性特征(准解理与沿晶分离形态)显著增强。充氢试样裂纹萌生区与扩展区均呈现韧窝、准解理和沿晶分离形态并存的混合断裂特征。
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.
