Chunbai Liu

The electric-tracked intelligent detection device is extensively used in engineering exploration and construction. However, in unstructured and complex terrains, its stability is challenged by a high center of gravity and poor road conditions, making the enhancement of its stability analysis crucial. This study is based on tracked vehicle models, constructing kinematic and dynamic equations for the device, and developing virtual and physical prototypes. Combining theoretical analysis, virtual simulations, and physical testing, the study analyzes motion parameters under typical conditions through numerical calculations and simulations. It examines the device’s dynamic characteristics during rapid turns in unstructured terrains. The research identifies variations in motion parameters, determines dynamic stability thresholds, and refines stability analysis methods applicable to the device, establishing evaluation criteria with multiple parameters and weight vectors. In conclusion, the developed models and prototypes help us understand the electric-tracked intelligent detection device’s dynamic characteristics and instability thresholds, enhancing its stability and operational efficiency and providing a theoretical foundation for related engineering machinery development.

Inspection robots, which improve hazard identification and enhance safety management, play a vital role in the examination of high-risk environments in many fields, such as power distribution, petrochemical, and new energy battery factories. Currently, the position precision of the robots is a major barrier to their broad application. Exact kinematic model and control system of the robots is required to improve their location accuracy during movement on the unstructured surfaces. By a virtual engine and digital twins, this study put forward a visualization monitoring and control system framework which can address the difficulties in the intelligent factories while managing a variety of data sources, such as virtual–real integration, real-time feedback, and other issues. To develop a more realistic dynamic model for the robots, we presented a neural-network-based compensation technique for the nonlinear dynamic model parameters of outdoor mobile robots. A physical prototype was applied in the experiments, and the results showed that the system is capable of controlling and monitoring outdoor mobile robots online with good visualization effects and high real-time performance. By boosting the positional accuracy of robots by 18% when navigating obstacles, the proposed precise kinematic model can increase the inspection efficiency of robots. The visualization monitoring and control system enables visual, digital, multi-method, and complete real-time inspections in high-risk factories, such as new energy battery factories, to ensure the safe and stable operations.

The curing process of hair-pin motor stator insulation is critical, as residual stress increases the risk of partial discharge and shortens a motor’s lifespan. However, studies on the stress-induced defects during insulation varnish curing remain limited. This research integrates three-dimensional numerical simulations and experimental analysis to develop a curing model based on unsaturated polyester imide resin, aiming to explore the mechanisms of residual stress formation and optimization strategies. A dual fiber Bragg grating (FBG) sensor system is employed for simultaneous temperature and strain monitoring, while curing kinetics tests confirm the self-catalytic nature of the process and yield the corresponding kinetic equations. The multi-physics simulation model demonstrates strong agreement with the experimental data. The results show that optimizing the curing process reduces the maximum stress from 45.1 MPa to 38.6 MPa, effectively alleviating the stress concentration. These findings highlight the significant influence of the post-curing temperature phase on residual stress. The proposed model offers a reliable tool for stress prediction and process optimization in various insulating materials, providing valuable insights for motor insulation system design.

The softening phenomenon of age-hardening aluminum alloy-welded joints is severe during conventional fusion welding, which increases the likelihood of stress and strain concentration in the joint during the period of service, significantly reduces the mechanical properties compared to the base metal, and represents an obstacle to the exploration of the potential structural performance. This review paper focuses on an overview of the softening phenomenon. Firstly, the welding softening mechanism and the characteristics of age-hardening aluminum alloys are clarified. Secondly, the current main research methods that can effectively improve joint softening are summarized into three categories: low-heat-input welding, externally assisted cooling during welding, and post-weld treatment. The strengthening mechanism and performance change rule of age-hardening aluminum alloy joints are systematically analyzed. Finally, this paper considers the future development trends of further research on joint softening, and it is expected that interest in this topic will increase.