Browse Topic: Parts and Components
Additive manufacturing (AM) processes facilitate the production of components with high geometrical complexity, presenting substantial opportunities for innovation in demanding sectors such as aerospace and biomedical engineering. A significant challenge impeding their broader application is the characteristic surface roughness of as-fabricated parts, which results from the layer-wise construction and the presence of partially melted powder particles. While electrochemical polishing (EP) represents a viable post-processing technique for achieving a smooth surface finish, a comprehensive understanding of how the non-equilibrium microstructures characteristic of AM materials interact with the EP process remains incomplete. This investigation centers on the electrochemical polishing behavior of Ti-6Al-4V alloy fabricated by direct energy deposition (DED), utilizing a sodium chloride-ethylene glycol electrolyte. The findings reveal that the material's distinct engenders anisotropic anodic dissolution. This behavior is attributed to the differential electrochemical potentials among the constituent phases and their crystallographic orientations, which consequently narrows the operational process window for effective, uniform polishing. This preferential dissolution of certain phases results in the formation of a subtle, micro-scale topographical variation that mirrors the orientation of the original columnar grain structure. Notwithstanding this microstructural influence, the EP treatment proved highly successful in refining the surface finish, substantially decreasing the average surface roughness from 0.350 μm to 0.042 μm. Concurrently, the treatment led to a significant enhancement in the alloy's corrosion resistance, attributed to an oxide layer. These findings underscore the critical necessity of accounting for microstructural characteristics when developing optimized electrochemical polishing protocols for additively manufactured components.
With the development of controlled nuclear fusion technology, the tokamak device, as the most promising magnetic confinement fusion reactor for advanced engineering applications, requires remote maintenance of its internal components, which has become a key factor affecting both operational efficiency and safety. As a critical component directly exposed to high-temperature plasma, the divertor target plate needs to be periodically replaced and carefully maintained to ensure stable and reliable reactor operation. However, this region is subject to extreme conditions, including high temperature, high vacuum, and intense radiation, making conventional manual maintenance infeasible. This necessitates the development of intelligent and automated teleoperation systems. To address the automated assembly and disassembly requirements of divertor target plates, this study designs an integrated target plate actuator comprising key functional units: a positioning module, a screwing module, a quick-change module, and a passive compliance structure. The actuator achieves rapid and precise alignment with target plate holes, accommodates bolts of different specifications, and exhibits excellent impact resistance. Furthermore, stiffness and mechanical analyses, supported by finite element simulations, verify the actuator’s safety and reliability under high loads and impact forces. To further enhance operational performance, a segmented disassembly and assembly control strategy based on reinforcement learning is proposed, enabling the actuator to adaptively handle torque variations and ensure precise and stable bolt operations. The results demonstrate that the proposed actuator and control strategy significantly improve the accuracy, stability, and efficiency of target plate operations under complex working conditions, providing a reliable solution for automated divertor maintenance in tokamak devices.
This device belongs to the field of aviation materials technology and discloses a high-efficiency drilling equipment for manufacturing aviation materials, which includes a bottom plate. The top of the bottom plate is provided with a clamping structure and a positioning structure. The clamping structure includes a second moving plate, a third sliding groove, a second bi-directional screw, a fourth screw block, a clamping plate, and a rubber block. This device can hold materials through a clamping structure and effectively and quickly locate and drill holes through a positioning structure. By rotating the first threaded rod, it can drive the first screw block to move the U-shaped column. During the movement of the U-shaped column, it will drive the second threaded rod to move together. By rotating the second threaded rod, it will drive the second screw block to adjust the height of the connecting plate for drilling holes. By rotating the third threaded rod, it will drive the first moving plate to move, which will facilitate the multi-directional movement of the drill bit, improve the efficiency of drilling, avoid multiple position changes, and be beneficial for practical applications and operations.
This paper examines the temperature distribution during pipe cutting and the impact of the heat-affected zone on the mechanical microstructure and properties of steel pipes. Utilizing testing equipment such as K-type thermocouples, a MESTL-WELD thermocouple spot welding machine, and a DC5516H 16-channel temperature data logger, temperature tests were conducted on Φ 1016 × 17.5 mm X70M spiral seam submerged arc welded steel pipes and Φ 1016 × 21 mm X70M straight-seam submerged arc welded steel pipes. The results indicate that the maximum test temperatures during cutting were 953.8 °C and 1216.6 °C, respectively, with the duration of temperatures exceeding 400 °C at each test point not exceeding 30 seconds. By fitting the relationship curve between the peak temperatures of each test point and the cutting distance using the ExpDec3 model, it was found that the cutting distance corresponding to a temperature of 580 °C was 12 mm. Furthermore, mechanical microstructure and property tests were performed on the pipe body at different positions of the HAZ. Except for an anomaly in the yield strength of the rod-shaped tensile specimens of the Φ 1016 × 21 mm X70M welded pipe body, no other abnormalities were detected. Macroscopic metallographic examination revealed that the axial length of the HAZ at the end of the cut pipe did not exceed 7 mm. Microhardness testing showed significant fluctuations in the microhardness of the pipe body at the end of the cut pipe, while the microhardness of the pipe body beyond 10 mm from the end gradually returned to normal.
The inconsistency in bearing data distributions under diverse conditions often affects the representations of the faulty data and leads to indistinct decision boundaries and even negative transfer resulted from overlapping class distributions, greatly limiting the accuracy of the diagnosis model. To cope with the challenge, a pseudo-label-guided dual-supervised alignment (PDSA) method is developed for bearing fault diagnosis across diverse operating scenarios in this paper. To address the fixed alignment strategy issue, an adaptive distribution alignment layer is incorporated to ResNet18 to achieve dynamic data distribution alignment under varying condition, To enhance classification performances, a dual-supervised mechanism, comprising shallow-layer supervised contrastive learning is introduced through target domain pseudo-labels in target domain and deep-layer regularization class consistency. Experiments on two publicly available bearing datasets demonstrated this model realizes refined class-level alignment, strengthens fault states representation, and shows notable superiority in both accuracy and robustness.
Aligned with the “3060 dual carbon” goal, the rapid growth of new energy installation capacity in China’s western high-altitude regions has caused an urgent demand for UHV converter station construction. This paper suggests a prefabricated structural system by using embedded ear-shaped tongue-and-groove UHPC wall-column connections to meet the challenges of traditional cast-in-place concrete firewalls, such as prolonged construction periods and difficulty in quality control in harsh environments. The seismic performance of the connection was investigated through pseudo-static tests and finite element analysis. The results show that failure mainly occurs on the wall–column interface, with cracks mainly appearing at the wall panel corners. The scaled model demonstrated full hysteresis loops, indicating stable energy dissipation. The ear-shaped tongue-and-groove connection showed superior initial stiffness and ultimate load-bearing capacity (404.3 kN) compared with the straight-type connection (177.5 kN). An increase in the semicircular diameter improved load capacity, while the axial compression ratio had little effect. This study proposes a theoretical reference for the design and application of prefabricated valve hall structures in high-altitude regions.
Accurate prediction of ground settlement induced by rectangular pipe jacking, a prevalent trenchless technology in urban infrastructure development, remains a significant challenge. This study addresses this by developing and evaluating a robust machine learning (ML) framework. Leveraging 104 sets of field monitoring data from the Liuye Avenue West Extension rectangular pipe jacking project in Hunan, China, key construction parameters including jacking force, advance rate, and grouting pressure were utilized as inputs to predict ground settlement. A Particle Swarm Optimization (PSO) algorithm was integrated for automated hyperparameter tuning of six distinct ML models: standalone Least Squares Support Vector Machine (LSSVM), Backpropagation Neural Network (BPNN), Random Forest (RF), and their respective PSO-optimized counterparts. Comprehensive performance evaluation using Mean Squared Error (MSE), Mean Absolute Error (MAE), and Coefficient of Determination (R^2) revealed that the PSO-LSSVM hybrid model achieved superior predictive accuracy and generalization capability. Specifically, on the test dataset, the PSO-LSSVM model yielded an MSE of 0.367, MAE of 0.424, and an R^2 of 0.941. These findings demonstrate that the proposed PSO-enhanced LSSVM model significantly outperforms baseline models, offering a highly effective and reliable tool for predicting ground deformation in similar complex pipe jacking projects.
This test method provides a procedure for measuring no-load rotational breakaway torque of self-lubricating spherical bearings.
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