To address the issue of low accuracy in detecting defects of battery cell casings with low space ratio and small object characteristics, the low space ratio feature and small object feature are studied, and an object detection algorithm based on dual-coordinate attention and small object loss feedback is proposed. Firstly, the EfficientNet-B1 backbone network is employed for feature extraction. Secondly, a dual-coordinate attention module is introduced to preserve more positional information through dual branches and embed the positional information into channel attention for precise localization of the low space ratio features. Finally, a small object loss feedback module is incorporated after the bidirectional feature pyramid network (BiFPN) for feature fusion, balancing the contribution of small object loss to the overall loss. Experimental comparisons on a battery cell casing dataset demonstrate that the proposed algorithm outperforms the EfficientDet-D1 object detection algorithm, with an average precision improvement of 4.23%. Specifically, for scratches with low space ratio features, the improvement is 13.21%; for wrinkles with low space ratio features, the improvement is 9.35%; and for holes with small object features, the improvement is 3.81%. Moreover, the detection time of 47.6 ms meets the requirements of practical production.

The dynamic servo error of heavy-duty vertical machining centres is one of the decisive factors affecting the machining accuracy of large and complex parts. Due to the characteristics of large mass, large load, and the large travel distance of the machine tool, non-linear factors such as friction, backlash, and lateral shift are more likely to cause unstable behaviours such as stick-slip and oscillation of the servo feed system of the machine tool, and reduce the performance and servo accuracy of the motion axis. In this paper, to consider the influence of non-linear factors such as friction, backlash, and lateral shift, an appropriately simplified representation of the mechanical transmission system of the ball screw has been used. According to the control structure of the Siemens 840D numerical control system, a theoretical model of the servo feed system for the heavy-duty vertical machining centre was established based on three-loop control. Then, the single-axis and double-axis closed-loop simulation models of the servo feed system were built in Simulink, and the influence pattern of control parameters and nonlinear factors on the dynamic servo error was obtained through simulation analysis. Finally, the validity of the theoretical model for the servo feed system was verified through a comprehensive comparison of simulation and experimental outcomes. This encompasses an analysis of the control system Bode plots, critical stick-slip velocity, and tracking errors in the X-axis with linear motion. The validation provides theoretical guidance for parameter design and mechanical adjustments of the servo feed system in heavy-duty vertical machining centres.

Due to the large size and large span of heavy-duty machine tools, the structural deformation errors caused by gravity account for a large proportion of the static errors, and the influence of gravity deformation must thus be considered in the machine tool precision design. This paper proposes a precision design method for heavy-duty vertical machining centers based on gravity deformation error modelling. By abstracting the machine tool into a multibody system topology, the static error model of the machine tool is established based on the multibody system theory and a homogeneous coordinate transformation. Assuming that the static error of each motion axis is composed of two parts, i.e., the manufacturing-induced geometric error and the gravity deformation error, the machine tool stiffness model of the relationship between gravity and deformation error is developed using the spatial beam elements. In the modelling process, the stiffness coefficients and volume coefficients of the components are introduced to fully consider the influences of structural parameters on machine tool precision. Taking the machine tool static precision, the component stiffness coefficients and the volume coefficients as the design variables, based on the use of the worst condition method, error sensitivity analysis and global optimization algorithm, the optimal allocation of the static error budget of the machine tool and the structural design requirements of each component are determined, providing a valuable guide for the detailed structure design and manufacture processing of the machine tool components.

Diverse model factories have been established in universities and enterprises to support practical education across various fields. Increasingly stringent health and safety regulations have made practical equipment more complex and costlier. With the advancement of digital infrastructure, virtual reality (VR) technology has been widely adopted in education to simulate real-world environments. This study explores the application of VR technology in enhancing manufacturing process education. To achieve this, an interaction methodology based on the OPC UA standard is proposed to enable data exchange between virtual and physical environments. Additionally, a detailed workflow of the practice course, conducted in a physical model factory at North China University of Technology, is presented. This approach is particularly noteworthy because it allows students to validate simulated results using a physical system, rather than relying solely on virtual scenes to mimic real-world settings. Students were divided into two groups: a practice group using the proposed digital method, and a control group without digital tools. The number of mistakes from the practice group was 37% less than that of the control group. Statistical analysis of students’ grades and questionnaire responses concludes that the proposed methodology is valuable to improve students’ engagement and practical skills. The presented course is replicable for other training institutions.

With the advancement of Industry 4.0, 3D printing has become a critical technology in smart manufacturing; however, challenges remain in the integrated management, quality control, and remote monitoring of multiple 3D printers. This study proposes an intelligent cloud monitoring system based on the SharkNet dynamic network, IoT, and artificial neural networks (ANNs). The system utilizes a SharkNet dynamic network to integrate low-cost sensors for environmental monitoring to enable low-latency data transmission and deploys ANN models on the cloud for print quality prediction and process parameter optimization. Next, we experimentally validated the system using the Taguchi design and ANN-based analysis, focusing on optimizing printing process parameters and improving surface quality. The main results show that the designed system has a communication delay of 40–50 ms and 99.8% transmission reliability under moderate load, and the system reduces the surface roughness prediction error to less than 17.2%. In addition, the ANN model outperforms conventional methods in capturing the nonlinear relationships of the variables, and the system can be based on the model to improve print quality and productivity by enabling real-time parameter adjustments. The system retains a high degree of scalability in terms of real-time monitoring and parallel or complex control of multiple devices, which demonstrates its potential for applications in smart manufacturing.

A high vacuum environment safeguards the performance of special processing technologies and high-precision parts such as nanosecond laser processing, chip packaging, and optical components. However, it poses higher requirements for the machine tool, which makes the temperature control of machine tools an important goal in design and development. In this paper, the thermal properties of a large-scale 5-axis laser processing machine tool in a vacuum environment were investigated. The thermal contact resistance between parts is identified by the parametric simulation and experiment. The whole machine temperature field was then obtained based on the fluid–thermal coupling model and verified by experiment. The results showed that the thermal contact resistance of the motor and reducer with the water cold plate was 560 W/(m²∙°C) and 510 W/(m²∙°C), respectively, and the maximum temperature increase of the machine was 3 °C. Based on the results, the machine tool’s temperature increase prediction chart was obtained by simulation under different processing conditions such as cooling water flow rate, cooling water temperature, motor speed, and ambient temperature. It provides technical and data references for the research on the thermal stability of the machine tool in processing.