A unique discontinuous lamellar microstructure of titanium alloys consisting of lamellar colonies at prior β-Ti grain boundaries and internal interwoven α-laths is prepared by a TiH_(2)-based powder metallurgy metho...A unique discontinuous lamellar microstructure of titanium alloys consisting of lamellar colonies at prior β-Ti grain boundaries and internal interwoven α-laths is prepared by a TiH_(2)-based powder metallurgy method.The α-variants get various crystallographic orientations and become discontinuous during vacuum annealing at 700℃.Remarkably,nanoscale phase δ-TiH compound layers are generated between α-laths and β-strips,so that dislocations are piled up at the α/δ/βinterfaces during tensile deformation.This leads to dislocation slips being confined to individual α-laths,with differentslips and particularly pyramidal<c+a>slips being activated.The efficiency of wavy slip is promoted and the work hardening rate is enhanced.Finally,the combined effect of dispersed micro-shear bands and lath distortions is considered contributive for alleviating the stress concentration at grain boundaries,resulting in a high-promising synergy of enhanced ultimate tensile strength of 1080 MPa and good elongation to fracture of 13.6%.展开更多
Aiming at the shortcomings of traditional State of Health(SOH)prediction methods in nonlinear modeling and temporal dependence handling,this paper proposes a hybrid CNN-GRU model integrated with the Dung Beetle Optimi...Aiming at the shortcomings of traditional State of Health(SOH)prediction methods in nonlinear modeling and temporal dependence handling,this paper proposes a hybrid CNN-GRU model integrated with the Dung Beetle Optimization(DBO)algorithm(denoted as DBO-CNN-GRU)for lithium battery SOH prediction.Indirect health factors strongly correlated with SOH are extracted from the NASA public dataset,and their effectiveness is verified using Pearson and Spearman correlation coefficients.A CNN-GRU model is designed:the convolutional neural network(CNN)is used to capture local features,and the gated recurrent unit(GRU)is combined to model the temporal dependence of capacity degradation.Furthermore,the DBO algorithm is introduced to optimize the model’s hyperparameters,enhancing the global search capability.Experiments show that the DBO-CNN-GRU model achieves significantly better test performance on the NASA dataset than the single CNN,GRU,and LSTM models.展开更多
基金financially supported by the National Natural Science Foundation of China(Nos.52301145,52275329)the Applied Basic Research Program of Liaoning Province,China(No.2023JH2/101300158)+1 种基金the Fundamental Research Fund for the Central Universities,China(No.N2202010)the Key Research Programs of High Education Institutions in Henan Province,China(No.24A430017).
文摘A unique discontinuous lamellar microstructure of titanium alloys consisting of lamellar colonies at prior β-Ti grain boundaries and internal interwoven α-laths is prepared by a TiH_(2)-based powder metallurgy method.The α-variants get various crystallographic orientations and become discontinuous during vacuum annealing at 700℃.Remarkably,nanoscale phase δ-TiH compound layers are generated between α-laths and β-strips,so that dislocations are piled up at the α/δ/βinterfaces during tensile deformation.This leads to dislocation slips being confined to individual α-laths,with differentslips and particularly pyramidal<c+a>slips being activated.The efficiency of wavy slip is promoted and the work hardening rate is enhanced.Finally,the combined effect of dispersed micro-shear bands and lath distortions is considered contributive for alleviating the stress concentration at grain boundaries,resulting in a high-promising synergy of enhanced ultimate tensile strength of 1080 MPa and good elongation to fracture of 13.6%.
文摘Aiming at the shortcomings of traditional State of Health(SOH)prediction methods in nonlinear modeling and temporal dependence handling,this paper proposes a hybrid CNN-GRU model integrated with the Dung Beetle Optimization(DBO)algorithm(denoted as DBO-CNN-GRU)for lithium battery SOH prediction.Indirect health factors strongly correlated with SOH are extracted from the NASA public dataset,and their effectiveness is verified using Pearson and Spearman correlation coefficients.A CNN-GRU model is designed:the convolutional neural network(CNN)is used to capture local features,and the gated recurrent unit(GRU)is combined to model the temporal dependence of capacity degradation.Furthermore,the DBO algorithm is introduced to optimize the model’s hyperparameters,enhancing the global search capability.Experiments show that the DBO-CNN-GRU model achieves significantly better test performance on the NASA dataset than the single CNN,GRU,and LSTM models.
