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.

To address the issue of real-time deformation monitoring for combined heading dies for titanium alloy bolts, this paper established a hardware system to introduce an indirect measurement method. In detail, we designed the monitoring model of different materials and developed a software system based on the model to monitor and analyze the die deformation during the heading process. The real-time data acquisition and experimental validation demonstrate the reliability of the system and the feasibility of the method.

This study addressed the issues in the course of Mechanisms and Machine Theory, such as scattered knowledge points and lack of systematic organization, in engineering universities. We applied knowledge graph and Mind mapping techniques to organize and reconstruct the knowledge points, combining with the Rain Classroom application, to explore the construction of digital education ecology. Through Pre-class, In-class and Post-class teaching practices, the results show that the combination of knowledge graph, Mind mappings and Rain Classroom not only solves the problem of students' inability to understand the knowledge system of the course fully and effectively but also promotes lecturers' understanding and control of the overall curriculum, which can provide new ideas for the construction of digital education ecology in engineering universities.

Background: With the increasing demand for logistics automation, the application of AGVs (Automated Guided Vehicles) in the industrial field become more widespread. Methods: This paper presents a heavy-duty AGV with self-load/unload capability for 23 t sheet-metal stacks for BIW (body-in-white) press shops, elaborating its structural design, simulation, manufacturing and testing. Results: The AGV enables autonomous movement and precise positioning of payload transfer without human intervention, which can enhance logistics efficiency, operational convenience and production safety. Finite element analysis was utilized to optimize the AGV structural strength and motion stability, while experiments validated the system feasibility and reliability. The developed AGV distinguishes itself through its high payload automation, robust structural design, and cost-effective precision positioning. Structural integrity under 3° shop-floor gradient and eccentric loading was verified by FEA. Buckling analysis of the upright under the eccentric-loaded condition returned a first-mode load multiplier of 92.8, far exceeding that of the ISO 3691-4:2020. Maximum stress and deflection under emergency braking case remain below 168 MPa and 1 mm allowable, respectively. Experimental results demonstrate that the AGV operates efficiently even under complex working conditions—docking trials achieved a mean error of 3.19 mm (specification ±5 mm). Conclusion: the AGV satisfies all the static and dynamic strength requirements without any mass increase. This reduces labor costs and operational risks substantially and provides a practical solution for the automated upgrading of intelligent logistics equipment.

To address the stability requirements of laser cleaning systems for battery terminals in new energy vehicles, this paper focuses on the laser cleaning system integrated with a three-axis motion platform. By finite element analysis, modal analysis was performed, and an optimization scheme was developed. The results show that the average natural frequency of the top ten modes increased after optimization, effectively avoiding the 0-30 Hz low-frequency excitation resonance range. The dynamic response characteristics were significantly improved, providing a reliable technical basis for optimizing the performance of the cleaning system.