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Based on grain refinement and secondary phase strengthening, minor boron (B) was added to near β-Ti alloy to strengthen the alloys. Ti85Fe6Cu5Sn2Nb2 alloys with various B contents were designed, prepared by using a non-consumable vacuum arc melting furnace, and hot rolled at 800℃followed by quenching. The effects of minor B addition on the microstructure and mechanical properties of Ti85Fe6Cu5Sn2Nb2 alloy were investigated through microstructure observation, tensile mechanical test, fracture observation and transmit electron microscopy. The results reveal that minor B addition can refine the grains, improve the strength whereas the plasticity of the alloy is decreased. The alloy containing 0.15% (mass fraction)B possesses the better comprehensive mechanical properties(σ0.2=1105 MPa, δb=4.5%).With the increase of B content, the strength of the alloy is increased and reaches up to 1156 MPa. Orthorhombic TiB compounds are formed in the alloy, distributed in the β-Tialloy matrix. Upon deformation, the fracture of TiB phases, cutting and debonding of TiB phases to the alloy matrix, formed the fracture source, resulted in the decrease of the alloy plasticity.

Remaining useful life (RUL) prediction for bearing is a significant part of the maintenance of urban rail transit trains. Bearing RUL is closely linked to the reliability and safety of train running, but the current prediction accuracy makes it difficult to meet the requirements of high reliability operation. Aiming at the problem, a prediction model based on an improved long short-term memory (ILSTM) network is proposed. Firstly, the variational mode decomposition is used to process the signal, the intrinsic mode function with stronger representation ability is determined according to energy entropy and the degradation feature data is constructed combined with the time domain characteristics. Then, to improve learning ability, a rectified linear unit (ReLU) is applied to activate a fully connected layer lying after the long short-term memory (LSTM) network, and the hidden state outputs of the layer are weighted by attention mechanism. The Harris Hawks optimization algorithm is introduced to adaptively set the hyperparameters to improve the performance of the LSTM. Finally, the ILSTM is applied to predict bearing RUL. Through experimental cases, the better performance in bearing RUL prediction and the effectiveness of each improving measures of the model are validated, and its superiority of hyperparameters setting is demonstrated. [ABSTRACT FROM AUTHOR]

As one of the important components of train traction motor, the bearing plays a key role in ensuring the safety of train operation. At present, the existing fault diagnosis methods for train traction motor bearings have the disadvantages of poor feature extraction ability, slow diagnosis speed, and low diagnosis accuracy. To solve the above problems, an improved deep residual network (IDRN) is proposed in this paper. Firstly, a new residual convolution block (RCB) is proposed to improve the extraction ability of hidden features. Secondly, the squeeze and excitation block (SE) is embedded in the residual block to increase the sensitivity of the model to features. Lastly, Adan is selected as the optimizer to increase the convergence speed of IDRN. The train traction motor bearing bench built by our research team is selected for the experiment. The results show that the test accuracy of IDRN on the bearing dataset of train traction motor is 98.72%, which is at least 2.27% higher than other comparative models. The IDRN model converges with fewer iterations in the training process, shortening the entire experiment time. At the same time, the CWRU bearing dataset and the bearing dataset with natural defects are selected for comparative verification. The experimental results show that the IDRN model exhibits strong robustness and practicability on different datasets. [ABSTRACT FROM AUTHOR]

(Ti40Zr25Cu9Ni8Be18)100-xYx (x=0,1.0,1.5,2.0,3.0) alloy rods with 3mm in diameter were fabricated by copper mold casting method. The microstructures, nano-crystallization and mechanical properties of the alloys were investigated by X-ray diffractometry(XRD), scanning electron microscopy(SEM), transmission electron microscopy(TEM), differential scanning colorimetry(DSC) and uniaxial compressive test.The results show that with 1.0%(atom fraction, the same below) addition of Y, the alloy presents amorphous nature by X-ray examination, with 1.5% of Y addition, nano-crystallization of the amorphous alloy is induced. Crystalline phases with the size of 5-20nm can be observed distributed in the amorphous matrix by high resolution transmission electron microscopy. The alloy with 1.5% of Y addition presents the fracture strength of 1990MPa, at the plastic strain of 3% upon uniaxial compressive test. The number of shear bands is increased in the fracture surfaces of the alloys with Y addition. The interference of shear bands hinders inhomogeneous deformation, increases the fracture strength and plasticity of the alloy.