To address the issues of low efficiency, high cost, and insufficient data reliability in the on-road test method for dynamic monitoring of the On-Board Diagnostic (OBD) system of heavy commercial vehicles under the China VI emission standard (GB 17691—2018), a dynamic OBD torque and speed limitation verification system based on Hardware-in-the-Loop (HiL) and signal virtual injection technology is proposed. By constructing a collaborative test platform composed of a chassis dynamometer data acquisition device (K - BOX), a high-precision urea level simulator, a multi - protocol OBD diagnostic module, and automated test software, closed - loop verification of the torque and speed limitation strategies of the Engine Control Unit (ECU) under abnormal urea level conditions is achieved. Currently, this technology has been successfully applied to 3 models of light and heavy commercial vehicles of a certain vehicle manufacturer.

To address charging detection failures in a new energy commercial vehicle, the system analyzes the causes of issues such as Vehicle Identification Number (VIN) mismatch, single-gun charging failure, timeout of the Battery Management System (BMS) Charging Ready Message (BRO), and timeout of the BMS Stop Message (BST). Based on in-depth fault analysis, the process of translating analytical conclusions into specific, actionable process parameters is elaborated, thereby optimizing and establishing a standardized production line charging detection procedure. This procedure defines seven process steps, from delay waiting, VIN identification, and single/dual-gun charging tests to status switching, along with their critical time parameters, ensuring the comprehensiveness and efficiency of the detection work.

Commercial line laser scanners for cross-section measurement in the automotive industry predominantly employ full cross-section fitting. In scenarios characterized by strong straight-line features of double parallel cross-sections, this approach introduces redundant computation, and the closed algorithms are difficult to adapt to the dynamic optimization requirements of production lines. To address this issue, specifically for such scenarios, the Hough transform is introduced to directly detect local straight-line features, based on the camera calibration, image filtering, and laser centerline extraction techniques associated with the light-section method. While maintaining measurement accuracy, this strategy substantially reduces measurement time, achieving a profile measurement error of ≤0.15 mm for double parallel cross-sections and effectively improving the automated measurement level of car body cross-sections.

To address the challenges of low efficiency, high miss rate, and insufficient imaging stability associated with manual visual inspection in traditional automotive Body-In-White (BIW) joining processes, an intelligent inspection system based on computer vision and deep learning technologies is developed. An industrial camera imaging system is constructed with optimized optical parameters, and image preprocessing is realized using automatic masking techniques based on interactive image segmentation. Object detection is performed by incorporating an end-to-end object detection model (DETR). A full-stack data management architecture encompassing data acquisition, processing, storage, and visualization is established. Experimental results demonstrate that the system achieves a detection accuracy of over 99%, reduces task completion time by 80%, and lowers the miss rate by 67%. Both inspection efficiency and result consistency are significantly improved, satisfying the requirements for batch inspection of BIWs.

The welding quality of the positive electrode in lithium-ion batteries directly affects internal resistance, safety, and cycle life. To address issues such as insufficient strength, overwelding, and poor consistency caused by overly broad welding parameter windows (amplitude, energy, pressure) in current production, a Design of Experiments (DOE) method is introduced for process optimization. Initially, through a full factorial experiment with three factors and two levels, it is found that the initial parameter range (amplitude 30~60 μm, energy 40~100 J, pressure 30~60 MPa) is generally in an "over-welded" state, with amplitude identified amplitude identified as the most critical influencing factor. Based on this, the study further adopts the Box-Behnken response surface design to construct quantitative models for tensile force, peel force, and welding effectiveness within a narrowed parameter space. The results indicate that within lower parameter intervals, the contribution of energy and amplitude to welding quality shifts from negative to positive. Through multi-response variable optimization, the optimal process parameter combination is determined as amplitude 36 μm, energy 50 J, and pressure 40 MPa. Verification experiments confirms that under this parameter combination, the welded joints exhibit excellent mechanical properties, ideal foil adhesion rates, and significantly improved process stability. This provides a reliable basis for achieving refined control and mass production consistency in positive electrode welding.

To analyze the effect of light transmittance on the display performance of In-Mold Labeling (IML) films with hidden woodgrain patterns, a series of hidden woodgrain IML films with different light transmittance levels are prepared. Combined with subjective visual evaluation experiments, the influence of transmittance variation on the "hidden nature" of woodgrain and display clarity is systematically investigated. The experimental results show that when the light transmittance of the hidden woodgrain IML film ranges from 25% to 35%, visual interference from the woodgrain pattern is effectively reduced, while the clarity and color reproduction accuracy of the displayed content are significantly improved. Furthermore, the interaction patterns between light transmittance and factors such as background light intensity are revealed.

In response to the urgent demand for high-performance and cost-effective titanium alloys in the rail transit sector, this paper focuses on the recycling and utilization of TC4-DT titanium alloy returned scrap. A self-developed purification line is employed to process the turning scrap from ingot peeling. The purified scrap is blended with sponge titanium at a 30% ratio, followed by melting via a dual Electron Beam Cold Hearth Melting (EBCHM) and Vacuum Arc Remelting (VAR) process. Typical forgings are then prepared through open-die forging. To evaluate the removal capability of High-Density Inclusions (HDI) during EBCHM melting, a simulated test is designed. Results demonstrate that HDIs larger than 0.5 mm in diameter can be effectively captured by the cold hearth, while those smaller than 0.5 mm are dissolved under high temperature. High-precision industrial computed tomography is applied to conduct non-destructive inspection on three forgings. The results reveal a dense micro-structure, free from defects such as inclusions, pores, and cracks, confirming the reliability of the proposed purification and dual melting process.

To study the effects of a 200 ℃/1~10 h aging treatment on the mechanical properties, microstructure and corrosion resistance of 6082 aluminum alloy, tensile tests, intergranular corrosion tests are conducted, and SEM examination is performed on the tensile fracture surfaces. The results indicate that at 200 ℃ , the number of precipitated phases in the alloy matrix gradually increases with prolonged aging time, while the alloy strength first increases and then decreases. Additionally, the fracture morphology transitions from a ductile fracture dominated by numerous equiaxed dimples to a mixed fracture characterized by fine and shallow dimples along with tear ridges. As the aging time increases, the number of pitting corrosion pits and cracks on the alloy surface gradually increases and they interconnect. After peak aging, the number of corrosion pits and cracks significantly decreases, and the intergranular corrosion susceptibility of the alloy shows a trend of first increasing and then decreasing. Therefore, the under-aged state of 6082 aluminum alloy has both high strength and intergranular corrosion resistance, making it suitable for large-scale mass production applications in automotive parts.

Currently, significant fluctuations in vehicle models and configurations are observed across various automobile manufacturing plants. These fluctuations lead to operational redundancies within the Set Parts Supply (SPS) system, while newly constructed plants face difficulties in making SPS implementation decisions. Through efficiency and cost analysis, AI-assisted data calculation, and case validation, this study investigates the specific impacts of model and configuration variations on SPS operations. An optimized SPS evaluation model based on lineside space accommodation capacity is proposed. Furthermore, a systematic implementation strategy consisting of four sequential steps, namely data calculation, layout simulation, collaborative assessment, and benefit analysis, is established. The results demonstrate that AI-assisted analysis effectively processes multidimensional data, including lineside space constraints and material dimensions.

This paper takes a certain twin-wire spraying equipment as the object, starting with the mechanical structure, key functional units, and electrical control system, to conduct a systematic technical analysis. Focusing on the whole life cycle maintenance of the equipment, it analyzes the construction principles, failure modes, and improvement methods of core units such as the melting sprayer, temperature tester, wire feeder, driver, flow valve, and network module. It constructs a maintenance application system for preventive maintenance, condition monitoring, fault diagnosis, and system improvement. The research shows that building a "construction analysis-maintenance strategy-closed-loop improvement" trinity maintenance system from principle to principle is the key path to ensure the stable implementation of new processes and improve the comprehensive efficiency of equipment.