TiZr-based high-entropy alloys (HEAs) have emerged as promising energetic structural materials (ESMs) due to their unique combination of penetration capability and impact-induced energy-release behavior. However, a comprehensive understanding of the coupled deformation, fracture, and energy-release mechanisms in HEA-based ESMs, particularly for TiZr-based systems designed to balance mechanical strength and reactivity, remains elusive. In this work, three representative TiZr-based HEAs were fabricated via vacuum arc melting. A novel three-layer spaced target structure was designed to simulate combined metal-casing and internal circuitry systems. The underlying mechanisms governing the coupled mechanical-energetic response were investigated by integrating quasi-static and dynamic mechanical testing with post-mortem microstructural characterization and energy-release quantification. The flash duration and intensity of the energy release were further proposed as optical indicators to quantitatively characterize the impact-induced energy-release behavior of TiZr-based HEAs, where flash duration reflects the sustained energy-release capability and flash intensity represents the instantaneous energy-release intensity. The results demonstrate that TiZrNbWV exhibits the highest yield strength and superior dynamic mechanical performance, together with the most persistent energy-release behavior, whereas TiZrNbV exhibits the best plastic deformation capability and a slightly higher quasi-static fracture strength, along with the stronger instantaneous energy-release capability under low-velocity impact conditions. Ballistic tests indicate that penetration of the front aluminum (Al) plate is dominated by kinetic effects, whereas significant thermochemical energy release occurs during interaction between fragmented projectiles and the rear circuit board. Increasing impact velocity promotes projectile fragmentation and oxygen-assisted oxidation, significantly enhancing the energy-release intensity and post-penetration effects. This work provides insights into the design of HEA-based ESMs with improved mechanical and energetic performance for defense and aerospace applications.
The critical challenge of excessive junction temperature caused by ultra-high heat flux densities (> 100 W cm−2) in deep-sea LED fish-attracting lamp (FAL) arrays is addressed. This study proposes a hybrid thermal management scheme integrating interfacial micro-texturing, chimney effect convection, and heat pipe phase change heat transfer, achieving the unification of passive high-efficiency heat dissipation and pressure-resistant sealing. The FAL housing structure is reconfigured using topology optimization to construct chimney effect enhanced flow channels integrated with heat pipe bundle arrays, thereby establishing efficient heat conduction pathways from the phenolic resin substrate (PRS) to the structural periphery. Micro-element texture (MET) arrays are fabricated at the PRS thermal interface to enhance interfacial thermal conductance. Based on multi-physics coupled numerical simulation, a parametric mapping model correlating geometric topology with thermal performance is established through response interface methodology, enabling the parametric optimization of micro-texture configurations. A thermal interface performance-testing platform is constructed to validate the accuracy and reliability of the numerical model. Experimental results demonstrate that the integrated heat pipe technology effectively suppresses LED junction temperature rise; moreover, groove-type MET arrays oriented perpendicular to the gravity direction not only significantly increase the effective heat dissipation area but also optimize the dynamic characteristics of natural convection. This proposed solution reduces the maximum operating temperature of deep-sea FALs by 6.73 % compared with conventional structures, providing an effective engineering solution for thermal structural design of high-power illumination systems.
In this paper, a magnetic field device applicable to reciprocating magnetorheological polishing (RMRP) is designed, and the influence of excitation current as well as working gap on the device's magnetic field performance is systematically studied. Specifically, the structure of the RMRP magnetic field device is configured and elaborated in detail, and a series of magnetic field simulations are further employed to analyze the variations in magnetic induction intensity within the polishing area induced by excitation current and working gap. In addition, RMRP experiments are conducted to evaluate how excitation current and working gap affect the final polishing results. The simulation results for the magnetic field device indicate that magnetic induction intensity in the polishing area is positively correlated with excitation current, yet negatively correlated with the working gap. When either the excitation current or the working gap is held constant, the magnetic induction intensity within the polishing area exhibits a non-monotonic trend of initial increase followed by decrease as radial distance increases. The RMRP experimental results demonstrate that at an excitation current of 4 A, the surface roughness of the specimen is reduced significantly, dropping from an initial 327 nm to a final 42 nm. Correspondingly, when the working gap is adjusted to 0.75 mm, the surface roughness of the specimen decreases from 322 to 41 nm.
Shaft flexibility in a multi-support bearing-rotor system simultaneously alters the journal attitudes at multiple supports, thereby redistributing the hydrodynamic support conditions along the shaft. This study investigates this system-level transmission in a three-disk, four-support bearing-rotor system subjected to end loading. The eccentricity and inclination states of all four journals are determined simultaneously through a coupled equilibrium solution of a Timoshenko beam structural model and a finite-length Reynolds lubrication model, and the resulting load- and speed-dependent oil-film coefficients are introduced into a finite-element rotordynamic model. End loading produces a strongly nonuniform support response: at 3000 rpm, the stiffness and damping traces of the loaded-end support J4FWBW−0.00037 % and +0.00039 %, whereas the higher-order H1H2+0.0140 % and +0.1477 %, respectively. The contrast is explained by the markedly different generalized participation of the bearing locations in the corresponding modal branches. These results show that the global dynamic consequence of local oil-film strengthening is governed by hydrodynamic support redistribution and modal participation at the support locations rather than by the magnitude of the local bearing-stiffness change alone.
Effective vibration control and dynamic behavior management in the presence of parameter uncertainties are critical for high-speed motorized spindles. Although traditional robust design optimization minimizes dynamic response variability, it lacks explicit control over failure probabilities. To address these limitations, this study utilizes data from 1000 experimental runs to drive a reliability-based robust optimization investigation. Instead of assuming ideal distributions, uncertainties in bearing stiffness and damping are quantified using the Multi-Innovation Stochastic Gradient (MISG) method. These empirically identified distributions are propagated via Monte Carlo simulation and integrated with the Non-dominated Sorting Genetic Algorithm II (NSGA-II) to analyze the trade-offs between vibration suppression and reliability across target failure probabilities of 1 %, 5 %, and 10 %. The results indicate that tightening the target failure constraint from 5 % to 1 % yields diminishing returns, necessitating a 7.0 % increase in preload for only a marginal gain in response robustness. Comprehensive experimental validation across the spectrum of 1000–24 000 rpm confirms the full-range efficacy of the proposed framework. Specifically, statistical validation via 20 independent physical replications at the rated speed of 24 000 rpm demonstrates that the optimized design reduces the experimental mean peak vibration by 16.84 % and the response standard deviation by 29.78 % while successfully curtailing the failure probability from 55.44 % to 0.35 %. These findings demonstrate that integrating experimental identification with robust optimization improves both dynamic performance and engineering reliability in spindle-bearing systems.
Manipulating microrobots within narrow conduits has significant potential in medical and industrial applications, including disease diagnosis and treatment within body lumens and inspection of fluid transport pipelines. To address the challenges of operating in fluid environments, this paper proposes a method for suspending a magnetic robot within the two-dimensional plane of a fluid conduit. Two vertically arranged electromagnets generate a non-uniform magnetic field that drives the magnetic ball. Hall sensors and visual measurement jointly provide positional feedback to the controller. The effects of drive current and fluid velocity on the levitation characteristics of the magnetic ball are investigated experimentally. Results indicate that, when the magnetic ball is suspended within the fluid pipe, higher fluid velocity or greater distance from the electromagnet axis results in larger horizontal displacement of the ball. Compared with a single-electromagnet drive, the dual-electromagnet drive effectively constrains the horizontal displacement of the magnetic ball and reduces fluctuations during fixed-point suspension. The horizontal deviation is reduced by up to 58 %. Maximum horizontal fluctuations decrease from 0.35 to 0.21 mm, while maximum vertical fluctuations decrease from 0.20 to 0.11 mm. Furthermore, by adjusting the current of each electromagnet, the magnetic ball achieves precise point suspension and stepwise motion within a confined fluid domain, with position control errors below 0.1 mm. The dual-electromagnet drive demonstrates superior performance in positioning control of the magnetic ball, providing a robust basis for future applications.
A planar prismatic Rubik's cube mechanism (PPRCM) is defined as a Rubik's-cube-inspired mechanism whose rigid components are constrained to pure planar movements. Taking a typical PPRCM prototype as the research carrier, this paper elaborates on the composition, topological configuration, and reconfigurable motion process of the mechanism. The influencing factors of PPRCM's topological structure are abstracted, and the conditions for realizing reconfigurable motion are thereby derived. Based on the reconfigurable topological characteristics of PPRCM, a type synthesis method for the mechanism is proposed. By specifying the values of the topological influencing factors, PPRCMs with different topological structures are generated, and corresponding three-dimensional(3D) models are constructed to validate the rationality and effectiveness of the proposed method. The type synthesis method for PPRCM proposed in this paper not only enables the development of more novel PPRCM configurations but also enriches and improves the type synthesis theories for planar mechanisms.
An effective dynamic wear prediction model for helical gear pairs considering tooth surface modification is established by deeply integrating the Archard wear model, the loaded tooth contact analysis (LTCA) model, and the lumped-parameter dynamic model of helical gear pairs. The dynamic contact stress can be obtained by introducing the dynamic mesh force into the LTCA model. By substituting the dynamic contact stress, relative sliding distance, and dynamic wear coefficient into the Archard wear model, the wear depth can be determined. A cyclic tooth-surface-updating strategy is employed during the wear prediction process. The impacts of input speed and input torque on contact stress and tooth surface wear distribution are discussed. The changes in mesh stiffness, dynamic contact stress, and dynamic responses of unmodified and modified helical gear pairs before and after wear are investigated.
The optimal design indices of heavy-duty wave compensation parallel mechanisms usually involve trade-off relationships, making multi-objective optimization of great significance for practical engineering applications. This study proposes a multi-objective optimization approach tailored for a heavy-duty wave compensation parallel platform, which integrates an artificial neural network (ANN) surrogate model with an enhanced NSGA-III algorithm. Firstly, a decision-making model is constructed, incorporating the topological parameters and performance indices of the mechanism. Secondly, an explicit surrogate model is developed to establish the relationship between structural parameters and the physical responses of the system using an ANN, thereby enhancing the computational efficiency of the pose-space traversal of the parallel platform. Finally, the NSGA-III algorithm, integrated with reference points and an interference penalty mechanism, is utilized for high-dimensional parameter optimization, while the technique for order preference by similarity to ideal solution (TOPSIS) is employed to achieve collaborative optimization across different objectives. Comparative results indicate that NSGA-III outperforms the traditional NSGA-II algorithm in terms of evaluation metrics. Compared to the initial baseline design, the optimal solution achieves an 18.12 % improvement in dexterity, an 11.78 % reduction in peak driving force, and a 6.85 % enhancement in the condition number, all while reducing actuator stroke. This study provides a valuable reference for the optimal design and prototype development of heavy-duty wave compensation parallel platforms.
This paper proposes a composite control strategy combining a radial basis function (RBF) neural network and super-twisting sliding-mode control for high-precision trajectory tracking of the UR10 manipulator under parameter variations, nonlinear friction, and external disturbances. An RBF network is employed to approximate the lumped unknown nonlinear dynamics online, thereby reducing the equivalent disturbance upper bounds. Simultaneously, a super-twisting robust control term is introduced to compensate for approximation residuals and remaining disturbances, which ensures system robustness while effectively suppressing conventional sliding-mode chattering. Furthermore, a projection-based adaptive law is designed to guarantee the strict boundedness of the network weights. Based on Lyapunov theory, the finite-time convergence of the sliding variable and tracking error is proven. Simulations on a 6-degree-of-freedom UR10 manipulator – incorporating mass/inertia perturbations and Coulomb-viscous friction – demonstrate that the proposed method achieves high-precision tracking with small steady-state errors and smooth, chattering-free control torques, verifying its effectiveness and engineering applicability.