For optimal design of mechanical clinching steel-aluminum joints, the back propagation （BP） neural network is used to research the mapping relationship between joining technique parameters including sheet thickness,...For optimal design of mechanical clinching steel-aluminum joints, the back propagation （BP） neural network is used to research the mapping relationship between joining technique parameters including sheet thickness, sheet hardness, joint bottom diameter etc., and mechanical properties of shearing and peeling in order to investigate joining technology between various material plates in the steel-aluminum hybrid structure car body. Genetic algorithm （GA） is adopted to optimize the back-propagation neural network connection weights. The training and validating samples are made by the BTM Tog-L-Loc system with different technologic parameters. The training samples＇ parameters and the corresponding joints＇ mechanical properties are supplied to the artificial neural network （ANN） for training. The validating samples＇ experimental data is used for checking up the prediction outputs. The calculation results show that GA can improve the model＇s prediction precision and generalization ability of BP neural network. The comparative analysis between the experimental data and the prediction outputs shows that ANN prediction models after training can effectively predict the mechanical properties of mechanical clinching joints and prove the feasibility and reliability of the intelligent neural networks system when used in the mechanical properties prediction of mechanical clinching joints. The prediction results can be used for a reference in the design of mechanical clinching steel-aluminum joints.展开更多
锂电池储能装置在电网中承担削峰填谷、调频保电的重要工作,因此有必要预测其运行状态,为下一步制定运维与检修计划提供依据。为实现锂电池储能装置荷电状态(state of charge,SOC)与健康状态(state of health,SOH)的联合预测,首先分析...锂电池储能装置在电网中承担削峰填谷、调频保电的重要工作,因此有必要预测其运行状态,为下一步制定运维与检修计划提供依据。为实现锂电池储能装置荷电状态(state of charge,SOC)与健康状态(state of health,SOH)的联合预测,首先分析了预测电压与温度的必要性和SOC与SOH的关联性,然后提出采用长短期记忆(long short-term memory,LSTM)网络来预测电压与温度的变化,并增加锂电池储能装置历史运行状态作为特征量来提高预测精度。最后在电压与温度预测值的基础上,对SOC和SOH进行联合预测。仿真结果表明,相比小波神经网络、BP神经网络模型,所提出的预测方法在预测电压与温度变化时精度更高;相较于荷电状态的单独预测,提出的方法能够以较高的精度联合预测SOC和SOH。展开更多
Fatigue characteristics of A7N01 aluminium alloy welded joint were investigated and a fatigue crack initiation life-based model was proposed. The difference of fatigue crack initiation life among base metal, weld meta...Fatigue characteristics of A7N01 aluminium alloy welded joint were investigated and a fatigue crack initiation life-based model was proposed. The difference of fatigue crack initiation life among base metal, weld metal and heat affected zone (HAZ) is slight. Furthermore, the ratio of fatigue crack initiation life (Ni) to fatigue life to failure(Nf) is a material dependent parameter, 26.32%, 40.21% and 60.67% for base metal, HAZ and weld metal, respectively. Total fatigue life predicted using the presented model is in good agreement with the experimental data and that using Basquin’s model. The observation results of fatigue fracture surfaces, using scanning electron microscope (SEM), demonstrate that fatigue crack initiates from smooth surface due to welding process for weld metal, blowhole in HAZ causes fatigue crack initiation, and the crushed second phase particles play an important part in fatigue crack initiation in base metal.展开更多
基金supported by Guangdong Provincial Technology Planning of China (Grant No. 2007B010400052)State Key Laboratory of Advanced Design and Manufacturing for Vehicle Body of China (Grant No. 30715006)Guangdong Provincial Key Laboratory of Automotive Engineering, China (Grant No. 2007A03012)
文摘For optimal design of mechanical clinching steel-aluminum joints, the back propagation （BP） neural network is used to research the mapping relationship between joining technique parameters including sheet thickness, sheet hardness, joint bottom diameter etc., and mechanical properties of shearing and peeling in order to investigate joining technology between various material plates in the steel-aluminum hybrid structure car body. Genetic algorithm （GA） is adopted to optimize the back-propagation neural network connection weights. The training and validating samples are made by the BTM Tog-L-Loc system with different technologic parameters. The training samples＇ parameters and the corresponding joints＇ mechanical properties are supplied to the artificial neural network （ANN） for training. The validating samples＇ experimental data is used for checking up the prediction outputs. The calculation results show that GA can improve the model＇s prediction precision and generalization ability of BP neural network. The comparative analysis between the experimental data and the prediction outputs shows that ANN prediction models after training can effectively predict the mechanical properties of mechanical clinching joints and prove the feasibility and reliability of the intelligent neural networks system when used in the mechanical properties prediction of mechanical clinching joints. The prediction results can be used for a reference in the design of mechanical clinching steel-aluminum joints.
文摘锂电池储能装置在电网中承担削峰填谷、调频保电的重要工作,因此有必要预测其运行状态,为下一步制定运维与检修计划提供依据。为实现锂电池储能装置荷电状态(state of charge,SOC)与健康状态(state of health,SOH)的联合预测,首先分析了预测电压与温度的必要性和SOC与SOH的关联性,然后提出采用长短期记忆(long short-term memory,LSTM)网络来预测电压与温度的变化,并增加锂电池储能装置历史运行状态作为特征量来提高预测精度。最后在电压与温度预测值的基础上,对SOC和SOH进行联合预测。仿真结果表明,相比小波神经网络、BP神经网络模型,所提出的预测方法在预测电压与温度变化时精度更高;相较于荷电状态的单独预测,提出的方法能够以较高的精度联合预测SOC和SOH。
文摘Fatigue characteristics of A7N01 aluminium alloy welded joint were investigated and a fatigue crack initiation life-based model was proposed. The difference of fatigue crack initiation life among base metal, weld metal and heat affected zone (HAZ) is slight. Furthermore, the ratio of fatigue crack initiation life (Ni) to fatigue life to failure(Nf) is a material dependent parameter, 26.32%, 40.21% and 60.67% for base metal, HAZ and weld metal, respectively. Total fatigue life predicted using the presented model is in good agreement with the experimental data and that using Basquin’s model. The observation results of fatigue fracture surfaces, using scanning electron microscope (SEM), demonstrate that fatigue crack initiates from smooth surface due to welding process for weld metal, blowhole in HAZ causes fatigue crack initiation, and the crushed second phase particles play an important part in fatigue crack initiation in base metal.
