The welding of 7075 aluminum alloys is highly susceptible to porosity, cracking, and grain coarsening, which severely impact joint integrity. To address these challenges, this study introduces a tri-coil composite magnetic field-assisted arc welding technique, designed to optimize weld morphology and mechanical properties. A multi-directional electromagnetic excitation device was developed to generate pulsed magnetic fields with a 180° phase difference, enabling precise control over arc oscillation and molten pool dynamics. The effects of varying excitation currents (0A, 2A, 5A, 8A) on weld formation, microstructural evolution, and mechanical performance were systematically investigated. The results indicate that under a 5A excitation current, the Lorentz forces effectively suppressed arc dragging, enhanced molten pool turbulence, refined the grain structure (reducing average grain size from 148.8 to 98.0 μm), and significantly reduced porosity (pore area decreased from 1653.84 to 204.13 μm2). Consequently, the welded joints exhibited notable improvements, with yield strength, ultimate tensile strength, and post-fracture elongation increasing by 14.05

This study investigates laser-cladded high entropy alloy (HEA) coatings on high-speed train axles to enhance wear resistance under specific fretting conditions. Axles in humid and acidic environments absorb hydrogen, leading to accumulation in grain boundaries, which weakens their structure and causes damage under alternating stress. Despite this, the impact of hydrogen damage on the fretting wear behavior of HEA coatings has not been explored. To address this, we performed fretting wear tests on a laser-cladded FeCoCrNiMo0.2 coating and a GCr15 steel ball friction system, evaluating their performance before and after hydrogen exposure. The results of the study indicate that under a constant load of Fn = 10N and a displacement amplitude of D = 50 mu m, the friction coefficient, maximum wear depth, wear volume, and wear rate increased when the system was in the hydrogen charging state compared to the non-hydrogen charging state. Specifically, the friction coefficient increased from 0.60 to 0.93, the maximum wear depth increased from 2.82 mu m to 3.63 mu m, the wear volume increased from 14.106 x 104 mu m3 to 22.098 x 104 mu m3, and the wear rate increased from 28.213 x 10-6 mm/Nm to 36.600 x 10-6 mm/Nm. Under the hydrogen charging state, the friction coefficient, maximum wear depth, wear volume, and wear rate all increased. This is due to hydrogen damage, including the formation of pitting pits and cracks on the surface of the coating, stress concentration, and brittle failure caused by hydrogen infiltration into the material. The presence of hydrogen makes the surface of the coating more prone to detachment, resulting in finer wear debris, deeper grooves, and increased oxidation. These factors accelerate the wear of the coating. This finding will contribute to the development and improvement of advanced surface modification techniques for materials in the hydrogen environment.

In the field of mechanical processing, parts grinding is a critical step to enhance the smoothness and precision of part surfaces. However, existing technologies face limitations in terms of informatization and intelligence, which hinder the optimization of grinding efficiency and quality. To address the technical challenges of trajectory generation for complex surface polishing, this paper proposes a reinforcement learning-based method. The approach utilizes a Deep Q-Network to optimize trajectory planning strategies, achieving adaptability and efficiency. Experimental results demonstrate that, compared to traditional manual teaching and offline programming methods, the proposed method significantly enhances dynamic adaptability and trajectory planning accuracy for complex workpieces, especially in large-scale tasks, showcasing superior performance and scalability. The findings highlight that reinforcement learning provides a novel approach for robotic polishing trajectory generation, laying a foundation for the advancement of intelligent manufacturing.

The paper introduces a robot autonomous grinding system for EMU train bogie welds. This system combines visual recognition technology and offline programming technology to achieve vision-guided teaching-free function and greatly improves the intelligence and efficiency of grinding operations. The traditional grinding process often relies on manual teaching or simple programming, which is difficult to deal with complex situations such as workpiece size differences and positioning deviations. This system uses a 3D vision camera to perform high-precision scanning of bogie welds, obtain weld position and shape information in real time, and quickly match it with the preestablished offline programming model to automatically generate a grinding path suitable for the current workpiece. This process can realize the precise positioning and operation of the grinding robot without manual intervention, effectively solving the problems of insufficient precision and low efficiency in traditional grinding methods. Experimental verification shows that this system can significantly improve the polishing quality of EMU train bogie welds, providing strong support for the intelligent upgrading of the rail transit manufacturing industry.

To explore the influence of electrode positive/electrode negative (EP/EN) ratio on the CMT advance-based wire arc additive manufacturing (WAAM) process for Al–Zn–Mg–Cu alloy, the behaviors of the deposition process, the microstructure and mechanical properties of Al–Zn–Mg–Cu alloy WAAM are studied. The results showed that electrical waveforms and arc/metal transfer were unstable with a low EP/EN ratio, which deteriorated the stability of WAAM. The forming accuracy of deposited samples firstly increased with the EP/EN ratio ranging from 4:16 to 13:7 and then decreased if the EP/EN ratio continued increasing. In the inter-layer region, the samples showed a combination of columnar grains and pony-size equiaxed grains with an EP/EN ratio ranging from 4:16 to 10:10, while the size and quantity of equiaxed grains increased and the columnar grains almost disappeared with a higher EP/EN ratio (≥ 13:7). The enriched alloy elements in the grain boundary were easy to form large-scale precipitated phases, which were harmful to the mechanical properties. The microhardness distribution of all samples had the apparent fluctuation along the building direction due to the existence of the microstructure evolution and pores defect. This study proved that the CMT advance process was a practicable and effective way for improving the performances of Al–Zn–Mg–Cu WAAM components with optimal EP/EN ratio.

A new type of three-winding magnetic field-assisted welding technology is proposed. When the welding speed is 1.2 m / min and there is no magnetic field assistance, the weld shows defects such as pores and cracks, with coarse columnar grains. After applying 80-160 Hz magnetic field, the grains are refined, and the porosity is reduced. However, the 240 Hz high-frequency magnetic field leads to a sharp increase in pore area and a decrease in mechanical properties. The results reveal that the medium- and low-frequency magnetic field drives arc oscillation and molten pool stirring through Lorentz force, promotes pore escape, and refines equiaxed grains.

In order to solve the problem of bearing fault diagnosis caused by frequent attitude change and working conditions of welding robots, a fault diagnosis method with deep integration of multiple information is proposed. Firstly, the variational mode decomposition (VMD) method is used to decompose the original vibration signal into multiple IMF components, and the short-term Fourier method (STFT) is used to convert it into a time-frequency diagram and fuse it, and then it is input to the CNN model with attention mechanism for diagnostic analysis. Finally, the results of the fault diagnosis of the welding robot bearing show that the proposed method not only has high diagnostic accuracy, but also outperforms the fault diagnosis method based on multiple information fusion in the time domain and frequency domain, respectively.

Failures of different components of the welding robot system may cause the same welding defects, which will waste time and personnel energy in troubleshooting the causes of welding defects. In order to solve this problem, this paper proposes a method to predict robot equipment failure based on welding defects based on knowledge graph theory. This method first uses statistical ideas to preprocess manually recorded historical data to obtain the proportion of different robot component failures in each type of welding defect. Then the knowledge graph method is used to establish a relationship model between welding defects and robot equipment failures, and analyze the correspondence between welding defects and robot equipment failures. Finally, the welding defects produced during the robot welding process are input into the established relational model to predict the location of the robot equipment failure. Moreover, welding defects and robot equipment failures that occur again in actual production are allowed to be input into the established model, and new welding defects and equipment failures are also allowed to continue to be input into the established model. The established model can be continuously iteratively optimized through newly input welding defect and equipment failure data to continuously improve the completeness and accuracy of the relationship model. The practicality of the established relationship model is verified through the manually recorded welding defect and robot failure history data in actual production. The research results can be used to guide robot equipment failures caused by welding defects during the robot welding process.

In welding tasks, the repeated positioning precision of robots can generally reach the micron level, but the data of each axis during each operation may vary. There may even be out-of-control situations where the robot does not run according to the set welding trajectory, which may cause the robot and equipment to collide and be damaged. Therefore, a real-time judgment method for the welding robot trajectory is proposed. Firstly, multiple sets of axis data are obtained by running the welding robot, and the phase of the data is aligned by using a proposed algorithm, and then the Kendall correlation coefficient is used to identify and remove weak axis data. Secondly, the mean of multiple sets of axis data with strong correlation is calculated as the standard trajectory, and the trajectory threshold of the robot is set using the μ ± nσ method based on the trajectory deviation judgment sensitivity. Finally, the absolute difference between the real-time axis trajectory and the standard trajectory is used to determine the deviation of the running trajectory. When the deviation reaches the threshold, a forewarning starts. When the deviation exceeds the threshold + σ, the robot is stopped. Take the six-axis welding robot as an example, by collecting the axis data of the robot running multiple times under the same conditions, it is proved that the proposed method can accurately warn the deviation of the running trajectory. The research results have important practical value for the prevention of welding robot accidents in industrial production.

Deep learning, due to its excellent feature-adaptive capture ability, has been widely utilized in the fault diagnosis field. However, there are two common problems in deep-learning-based fault diagnosis methods: (1) many researchers attempt to deepen the layers of deep learning models for higher diagnostic accuracy, but degradation problems of deep learning models often occur; and (2) the use of multiscale features can easily be ignored, which makes the extracted data features lack diversity. To deal with these problems, a novel multiscale feature fusion deep residual network is proposed in this paper for the fault diagnosis of rolling bearings, one which contains multiple multiscale feature fusion blocks and a multiscale pooling layer. The multiple multiscale feature fusion block is designed to automatically extract the multiscale features from raw signals, and further compress them for higher dimensional feature mapping. The multiscale pooling layer is constructed to fuse the extracted multiscale feature mapping. Two famous rolling bearing datasets are adopted to evaluate the diagnostic performance of the proposed model. The comparison results show that the diagnostic performance of the proposed model is superior to not only several popular models, but also other advanced methods in the literature.

焊接技术是轨道交通装备生产制造中非常重要的一个环节.由于轨道交通装备通常都是大型结构件,其焊接的复杂程度和质量要求都是极高的.为了解决上述问题,开发了一套基于桁架结构和串联工业机械臂复合的面向大型结构件的智能焊接机器人系统.文中主要介绍面向大型结构件的智能焊接机器人系统的硬件组成,包括桁架机器人、6自由度机械臂、变位机和焊接系统等.此外,还重点介绍了智能焊接机器人的控制系统.最后介绍了面向大型结构件的智能焊接机器人系统的工作流程等.

Traditional gas metal arc welding (GMAW) and compound external magnetic field (CEMF)–assisted GMAW were used to manufacture S355J2W steel T-joint bogie structures. The weld formation, microstructure, and mechanical properties of the joints were investigated during welding along with the arc behaviors and temperature distribution. The results show that CEMFs can suppress spatter formation and undercut defects when the welding speed increases by a factor of two. Forward tilting and periodical swinging of the arc create a more uniform temperature distribution of the weld pool, which decreases the Marangoni force. The stirring effect of the CEMFs changes the solidification and crystallization process of the weld pool and eventually results in grain refinement, which improves the microhardness and ultimate tensile strength of the weld joint. Thus, CEMF-assisted GMAW can improve the welding efficiency in bogie manufacturing with T-joints and single-bevel grooves.

Optimizing forming accuracy and improving deposition rate are the research hotspots in directed energy deposition (DED) of the aluminum alloy structures with medium-to-large size. This work employed a self-designed external compound magnetic fields (ECMFs) to enhance DED efficiency and forming accuracy simultaneously. Compared with the results of gas metal arc welding-based conventional-deposition-rate DED process with a low wire feeding rate, the deposition rate of the ECMFs assisted DED process was increased by 93.42%, surface roughness for each side of the cross-sectional samples was decreased by 52.86% and 82.73%, and the average grain size in the top and middle regions was decreased by 5.18% and 52.48%, respectively. The reasons for the high forming accuracy and acceptable mechanical property were explained distinctly. This novel method realized the high deposition rate of the DED process with good formability and performance.

The measurement and control of residual stresses are crucial to the structural safety of high-speed trains. The critical refraction longitudinal wave method is extensively employed for the residual stress measurement, and the correction of the influencing factors is the key to the detection accuracy. However, the existing methods mostly give purely mathematical expressions which are only applicable to their studied materials. Hence, this paper proposes the specific influence factor correction method to enhance the applicability and accuracy, and the 5083 aluminum alloy welded component is utilized for testing. Subsequently, the stress coefficient K and the compensation acoustic time under the influence of internal factors are obtained by employing the proposed method, combined with the simulation to determine the focused detection zone, the hole-drilling and X-ray methods are utilized for comparisons, and the results indicate that the test data have a good coincidence. Meanwhile, the detection errors of each zone before and after the correction are analyzed. Moreover, combined with the experimental verification, it is found that the penetration depth of a critical refraction longitudinal wave approaches its one wavelength; the corresponding study is conducted with this characteristic and concludes that in the weld zone, the longitudinal residual stresses are mainly concentrated on the surface of the measured material. Finally, the above results indicate that the proposed method can provide more accurate measurements for engineering applications.

本文综合了当前可靠性分析及评价方法,分别从可靠性目标、流程、准备工作、建模及分析评价等方面,凝练总结了可靠性分析方法实施过程中的一些共性要求,规范了相关流程,为工程中实施和推广可靠性提拱了参考和指导.

The magnetic field generator was designed and assembled on the welding torch to produce perpendicular and parallel magnetic fields periodically in the arc to improve the weld formation of 6N01-T6 aluminum alloy at high-speed welding. The mechanism of this hybrid weld process was studied based on the analysis of arc behavior and bead formation at different excitation parameters. The microstructure, tensile strength and impact toughness of joints were tested and analyzed. Results showed that the compound external magnetic fields suppressed the weld-bead defects effectively and increased the welding speed from 0.75 m/min to 2.1 m/min. The stirring effect of compound external magnetic fields had the advantage of refining grains in the weld zone, which improved the tensile strength and impact toughness of joint at high-speed welding. It proved that the compound external magnetic fields assisted melt inert-gas welding was a high-speed and low-cost welding technology for aluminum alloy.

转向架是高速动车组最为重要的部件之一,而构架是转向架的骨架,是衡量高速动车组研制水平和制造能力的关键指标.转向架构架焊接成型制造包括组装、焊接、打磨、检测等工艺,并分布在不同的作业区域.运用仿真推演与重构、哑终端智能改造、M2M交互集成、"推-拉"智能物流、健康监测、云边协同等智能制造技术和模式,实现了构架组"焊-磨-检"工艺一体化柔性成线,并得到了应用验证,可以在工程机械、船舶等行业应用推广.

TIG-MIG hybrid welding produces better weld formation than traditional MIG welding at higher welding speeds. The adaptive heat source and arc pressure models of inclined TIG-MIG arcs on curved surface were improved based on their distribution on flat surface. A 3D transient model of heat and mass transfer in a weld pool was established to analyze the influences of various welding parameters on a high-speed TIG-MIG hybrid welding process. The influence of the TIG current, distance between electrodes, and welding speed on this hybrid welding process were simulated to study the quantitative relationships between process parameters, molten pool behavior, and weld beam formation. The heat flux field, temperature field, and fluid flow on the upper surface and longitudinal sections of the weld pool were simulated and analyzed under various welding conditions. Experiments were performed; the simulation results agreed well with the experiments.

随着自动化技术不断发展,智能物流系统成为智能车间的重要组成部分.物料搬运是生产过程中必不可少的活动,物流搬运的效率和质量直接影响生产效益和生产成本.本文针对智能车间多品种小批量的生产特点,提出了一种基于空中穿梭车的物流模式,设计了空中穿梭车运动机构、电控系统和调度策略.空中穿梭车的特点是集物料抓取、卷绕升降、自动输送等多功能于一体,从而形成空中物流方案,解决了传统AGV物流模式占用地面空间、运行障碍多、配送效率低等问题,实现智能车间多品种变批量物流需求.

为了消除信息孤岛,解决各应用系统之间的信息共享、流程驱动等问题,对信息系统集成的层次、方法及技术进行探讨,并介绍相关的系统集成标准.基于企业服务总线(ESB)集成中间件,采用WebService规范接口,实现转向架构架智能焊接群控系统与产品生命周期管理(PLM)、制造执行系统(MES)以及质量管理系统(QMS)集成.实现焊接生产过程计划与执行的闭环管理,解决工艺路线及相关工艺数据、质量策划数据以及质量记录数据的共享和系统互联问题.