Interior Permanent magnet synchronous motors (IPMSMs) have become the preferred powertrain solution for electric vehicles due to their exceptional performance characteristics. However, the high-frequency electromagnetic noise generated during motor operation poses a significant challenge to occupant comfort within the vehicle. This study provides a comprehensive analysis of the electromagnetic forces, modal characteristics, and vibration noise for a 12-pole, 36-slot IPMSM, incorporating theoretical and simulation-based approaches as well as modal tests. By innovatively combining orthogonal experimental design with nonparametric regression techniques, a response surface model is developed to accurately characterize and optimize the radial electromagnetic force harmonics of the motor. The optimization results reveal a significant 37.7 % reduction in the motor’s surface vibration velocity and an 8.5 % decrease in peak noise levels, successfully meeting the engineering objectives for vibration and noise attenuation. This study not only contributes to the advancement of noise control technologies in electric vehicle power systems but also provides novel insights and methodologies for motor design, offering significant practical value and engineering relevance.

Addressing the issue of low bonding strength in Ti/Mg laminated composites due to interfacial oxidation, this study employs a differential temperature rolling method using longitudinal induction heating to fabricate Ti/Mg composite plates. The entire process is conducted under an argon gas protective atmosphere, which prevents interfacial oxidation while achieving uniform deformation. The effects of reduction on the mechanical properties and microstructure of the composite plates are thoroughly investigated. Results indicate that as the reduction increases, the bonding strength gradually increases, mainly attributed to the increased mechanical interlocking area and a broader element diffusion layer. This corresponds to a transition from a brittle to a ductile fracture at the microscopic tensile–shear fracture surface. When the reduction reaches 47.5%, the Ti/Mg interfacial strength reaches 63 MPa, which is approximately a 20% improvement compared to the bonded strength with previous oxidation at the interface. Notably, at a low reduction of 17.5%, the bonding strength is significantly enhanced by about one time. Additionally, it was found that a strong bonded interface at a high reduction is beneficial in hindering the propagation of interfacial cracks during tensile testing, enhancing the ability of the Ti/Mg composite plates to resist interfacial delamination.

The thermal error of the high-power grinding motorized spindle, caused by heating, seriously affects machining accuracy. In this paper, an ensemble learning algorithm is used to predict the thermal error of a high-precision motorized spindle. The subsequent problem of thermal error compensation can be effectively solved by a suitable thermal error model, which is crucial for improving the machining accuracy of the actual machining process. Firstly, the steady-state temperature field of the grinding motorized spindle is analyzed and used to determine the position of the sensors. Then, a signal acquisition instrument is used to monitor real-time temperature data. After that, experimental results are obtained, followed by verification. Finally, based on experimental data and the optimization results of temperature measurement points, temperature data are used as the input variable, and thermal deformation data are used as the output variable. The ensemble learning model is composed of different weak learners, which include multiple linear regression, back-propagation, and radial basis function neural networks. Different weak learners are trained using datasets separately, and the output of the weak learners is used as input to the model. Through integrating strategies, an ensemble learning model is established and compared with a weak learner. The error residual set of the ensemble learning model remains within [−0.2, 0.2], and the prediction performance shows that the ensemble learning model has a better predictive effect and strong robustness.

In this paper, the dynamics of a mechanical exciter and three cylindrical rollers (CRs) with the non-identical friction coefficients interacting through a rigid platform is considered. Sufficient conditions for the existence and stability of synchronous solutions in the coupled system are derived by using the average method of modified small parameters and Routh-Hurwitz principle. The obtained theoretical results are illustrated and analysed based on numerical calculations. In the analysis, the numerical results are presented for simple one-parameter variation, as well as for a group of varied parameters, when the influence of the coupling structure’s parameters on synchronization and stability is studied. An appropriate selection of the key parameters will eventually lead to desired synchronization performance. Finally, the theoretical and numerical results are supported by computer simulations. The stable synchronized states can be observed in the simulations even when there are unavoidably small differences in the three friction coefficients. If we mismatch the friction coefficients of the CRs, they are seen to synchronize with a constant phase difference. The key feature of the proposed coupled system is the dynamic coupling torque, which serves as the vehicle for transferring energy from an induction motor to three CRs without the direct driving sources and the synchronization controller for maintaining the originally synchronous and stable states against the disturbance in the simulations.

Acoustic signal-based fault diagnosis offers a promising non-contact approach for rotating machinery. However, its practical application is usually affected by environmental noise. This paper presented a Denoising Attention Domain Adversarial Network (DDAN) for the robust fault diagnosis of wheel hub motors under severe noise interference. The proposed framework consists of the following two core modules: a DenseNet-based denoising module that adaptively suppresses background noise while retaining critical fault features, and a Stacked Autoencoder Domain Adversarial Network (SADAN) that integrates channel attention, spatial attention, and multi-head self-attention (MHSA) for refined feature extraction and classification. Such a hierarchical attention mechanism facilitates effective local noise suppression and global dependency capture. Validation on a hub motor fault dataset and publicly available online dataset demonstrates that compared to existing methods, DDAN achieves superior diagnostic accuracy across various noise levels and signal-to-noise ratios, improving SNR from -15.97 dB to 1.24 dB, achieving 82.71% accuracy under low SNR condition, and reaching 84.93% and 83.75% accuracy in cross-domain generalization tests. Furthermore, the comparison of the diagnostic accuracy of audio signals from different acoustic acquisition devices further verifies the practicality and potential of the system in low-cost industrial deployment.

Addressing the issue of low bonding strength in Ti/Mg laminated composites due to interfacial oxidation, this study employs a differential temperature rolling method using longitudinal induction heating to fabricate Ti/Mg composite plates. The entire process is conducted under an argon gas protective atmosphere, which prevents interfacial oxidation while achieving uniform deformation. The effects of reduction on the mechanical properties and microstructure of the composite plates are thoroughly investigated. Results indicate that as the reduction increases, the bonding strength gradually increases, mainly attributed to the increased mechanical interlocking area and a broader element diffusion layer. This corresponds to a transition from brittle to ductile fracture at the microscopic tensile-shear fracture surface. When the reduction reaches 47.5%, the Ti/Mg interfacial strength reaches 63 MPa, which is approximately a 20% improvement compared to the bonded strength with previous oxidation at the interface. Notably, at a low reduction of 17.5%, the bonding strength is significantly enhanced by about 1 time. Additionally, it is found that a strong bonded interface at high reduction is beneficial in hindering the propagation of interfacial cracks during tensile testing, enhancing the ability of the Ti/Mg composite plates to resist interfacial delamination.