To address the problem of repeated manual alignment in the multi-angle surface machining of clutch housings, a single-alignment control method based on kinematics modeling for non-RTCP (Rotational Tool Center Point) machine tools is proposed. Unlike traditional processes that require manual re-alignment after each A-axis rotation, the proposed method first establishes and solves the machine tool kinematic model to derive accurate machining coordinate transformation formulas. A dedicated digital machine model is constructed, motion logic between kinematic axes is optimized, and an interactive interface between the machine tool and CAM (Computer Aided Manufacturing) software is developed. CNC (Computer Numerical Control) system macro-program secondary development is implemented for customized machining safety control, and a specialized post-processor is built by extracting key machining parameters. Tool paths are post-processed in CAM software, and generated NC (Numerical Control) programs are verified via simulation and actual machining. The results show that the method eliminates repeated manual alignment entirely, reduces setup time and human-induced errors, and adapts perfectly to non-standard modified machine tools, providing a practical and efficient solution for multi-face machining of clutch housings.

Vibro-impact capsule robots driven by dielectric elastomer actuators (DEAs) feature a simple structure and controllable bidirectional crawling capability. Despite the well-proven effectiveness of the multi-layer stacking technique in improving the output performance of the DEAs, whether this technique can also enhance the locomotion performance of the DEA-driven vibro-impact capsule robot remains unclear. In this work, a nonlinear dynamic model of the multi-layer-DEA-driven capsule robots is first developed. Together with experiments, this paper investigates the effects of DE layer number on the maximum velocities of the capsule robot. The study results show that increasing the number of layers in the DEA does not necessarily improve the maximum locomotion velocity of the capsule robot. In contrast, the maximum achievable velocity begins to reduce as the number of layers increases from two to three. This is mainly due to the different scaling effects of the impact force and the rebound force exerted by the DEA against the number of layers. The finding reported in this paper raises the necessity for developing novel solutions for enhancing the locomotion performance of the DEA-driven vibro-impact crawling capsule robot in the future.

Inspired by metasurfaces’ control over light fields, this study created a liquid microlens coated with a layer of Au@TiO2, Core-Shell nanospheres. Utilizing the surface plasmon resonance (SPR) effect of Au@TiO2, Core-Shell nanospheres, and the formation of photonic nanojets (PNJs), this study aimed to extend the imaging system’s cutoff frequency, improve microlens focusing, enhance the capture capability of evanescent waves, and utilize nanospheres to improve the conversion of evanescent waves into propagating waves, thus boosting the liquid microlens’s super-resolution capabilities. The finite difference time domain (FDTD) method analyzed the impact of parameters including nanosphere size, microlens sample contact width, and droplet’s initial contact angle on super-resolution imaging. The results indicate that the full width at half maximum (FWHM) of the field distribution produced by the uncoated microlens is 1.083 times that of the field distribution produced by the Au@TiO2, Core-Shell nanospheres coated microlens. As the nanosphere radius, droplet contact angle, and droplet base diameter increased, the microlens’s light intensity correspondingly increased. These findings confirm that metasurface coating enhances the super-resolution capabilities of the microlens.

The causes of tooth profile concave error during shaving machining are numerous and complex, and there are strong interaction effects among the influencing factors, which leads to significant deviations between the analysis of tooth profile concave error caused by single factors and the actual results. This paper establishes a shaving meshing model incorporating axis intersection angle error. Based on shaving cutting principles, a numerical coupling method is employed to integrate the multi-source factors contributing to shaving machining errors, coupling the degree of overlap, radial feed, and axis intersection angle error into the shaving tooth profile cutting depth error at the meshing point. A stochastic algorithm-optimized genetic algorithm (SA-GA) is subsequently utilized to solve the shaving tooth profile cutting depth error. The research results indicate that when the degree of overlap ranges from 1.3 to 1.9, the radial feed ranges from 20 to 50 μm, and the axis intersection angle error ranges from 0.2° to 0.5°, the workpiece gear tooth profile’s concave error is 5.973084 μm, which is smaller than the typical range of shaving tooth profile concave error (0.01–0.03 mm). By controlling the magnitude of the shaving tooth profile cutting depth error, the shaving tooth profile concave error can be effectively reduced. This method provides a new technical approach for minimizing the tooth profile concave error.

Vibro-impact capsule robots driven by dielectric elastomer actuators (DEAs) feature simple structures and high locomotion efficiencies. Stacking multiple layers of membranes on one DEA frame is a widely adopted approach to multiply the power and work output of the DEA, however, the effectiveness of utilizing a multi-layer DEA to boost the locomotion performance of DEAdriven vibro-impact capsule robots remains unclear. To this end, this paper first conducts a comprehensive numerical study on the locomotion performance of the multi-layer-DEA-driven vibro-impact capsule robots. Simulation results show the counterintuitive finding that the locomotion velocity of the robot reduces with the increasing number of layers in the DEA, and the possible factors leading to such reductions in velocity are analyzed. Next, this paper introduces a new driving scheme whereby multiple layers in The DEA are separated. Simulation results indicate that maximum velocity can be achieved by having the actuation voltages for one DEA lead the others by a relative phase between 50 degrees to 75 degrees as the dominant vibro-impact driving source. Improvements in the maximum velocity of the vibro-impact capsule robot by up to 70 % are achieved by using three DEAs. The key findings of the numerical studies are validated through experiments, and the consistency between the experimental and simulation results demonstrates the validity of the model.

This work developed a novel rocking-chair flow electrode capacitive deionization (R-FCDI) system that employs the commercial monovalent selective membrane (CMSM) to realize extremely efficacious separation of Li+/Mg2+ ions with extracting lithium from salt lake brine. The R-FCDI system exhibits exceptional lithium extraction performance, obtaining an electrosorption efficiency (ESR) of 98.4 %, an average electrosorption rate (AESR) of up to 7.77 mu mol cm- 2 min- 1, a high charge efficiency (CE) of 93.44 %, and energy consumption (Em) as low as 0.07 kWh mol- 1 Li. Especially, the Em is considerably lower than that of electrodialysis (ED, 0.13-3.795 kWh mol- 1 Li), making it a suitable option for industrial lithium extraction by reducing the operational costs. Moreover, the selectivity coefficient of the system reached a maximum value of 15.93 with low Em (0.037 kWh mol- 1 Li) when the magnesium/lithium mass ratio was 10 under a low applied voltage of 0.8 V. The electrochemical measurements reveal that the selective mechanism of Li+/Mg2+ ion in the proposed R-FCDI system is ascribed to the significantly higher capacitance of lithium ions (1.13-fold as greater as that of magnesium ions) and the substantially lower transmembrane transfer resistance of lithium ions (2.48-fold lower compared to that of magnesium ions). Interestingly, this study found that the initial ion concentration of the recovered solution has a marked consequence on the selectivity coefficient of the R-FCDI system, in which the highest selectivity coefficient of 34.99 with low Em (0.042 kWh mol-1 Li) was achieved when the ion concentration is 500 mg L-1. In an experimental study of the natural brine from Golmud salt lake, China, the maximum of selectivity coefficient is 24.74 and the Em is 3.03 kWh mol-1 Li at optimal process parameters.

The influencing factors of shaving-induced mid-profile concavity error and tooth profile errors are numerous and complex, yet they lack effective interconnections, resulting in severe siloization issues. Existing literature research and practical manufacturing data have primarily accumulated the relationship between the influence of individual factors on gear shaving error and stored in the form of text dispersed. These discrete knowledges can't be systematically integrated and utilized and individual factor influence mechanism can't be effectively shared, resulting in the failure to quantitatively differentiate between the factors of the role of the mechanism, while the unclear underlying mechanisms obstruct the fundamental revelation of the mapping relationship between shaving-induced mid-profile concavity error and tooth profile errors. To address this, this study derives computational formulas for the improved gear shaving tooth profile cutting depth error and shaving allowance, solving the gear shaving tooth profile cutting depth error and shaving allowance by the shaving cutting parameter (radial feed), and analyzing the influence law of radial feed on the gear shaving tooth profile cutting depth error and shaving allowance. Experimental results demonstrate that when the degree of overlap ranges from 1.3 to 1.9, and the axis intersection angle error falls within 0.2°~0.5°, selecting a radial feed of 44 ~ 50 μm simultaneously reduces tooth profile middle-concave error and tooth profile errors in workpiece gears. To resolve the lack of factor correlation, the Neo4j graph database was employed to construct a knowledge graph of influencing factors for shaving-induced mid-profile concavity error and tooth profile errors. This framework enables unified management of shaving error factors, the expression of correlations between knowledge entities, and relevant search and reasoning. It effectively resolves the issue of missing intrinsic connections among shaving error factors, realizes efficient retrieval and reasoning of knowledge regarding the intrinsic mechanism through which shaving error factors induce gear shaving errors, and thereby better facilitates the solution to tooth profile errors in shaving processes.

Binder-Free Electrodes In article number 2306530, Anjiang Cai, Hongjian Zhou, and co-workers constructed binder-free electrodes by in-situ growth of the LiMn2O4 materials with highly ordered hierarchical nanostructures on a highly flexible conductive carbon cloth substrate as cathode in a hybrid capacitive deionization cell for selectively extracting lithium from salt-lake brine.

A novel approach for fabricating deformable microlens arrays using template-induced self-assembly technology is introduced. By utilizing shear flow at cavity openings and hydrophobic sliding at interstitial gaps, this method achieves precise liquid microlens formation without the need for complex mechanical systems. The process employs a template immersed in a glycerol bath, where interfacial forces enable the pinning of liquid droplets within specific microcavities. A combination of theoretical modeling and finite element simulations was used to investigate the effects of cavity radius, pulling speed, and static contact angle on microlens liquid height. Findings reveal that larger cavity radii and higher static contact angles enhance liquid height. However, increased pulling speeds initially raise the liquid level before a subsequent decrease. Experimentally, uniform microlens arrays were successfully fabricated, demonstrating consistent surface shapes with the liquid level height of 43 mu m and remarkable imaging tunability under thermal stimuli, achieving a 2.22-fold focal length expansion. This work advances the understanding of droplet manipulation and offers promising applications in microfluidics, optical systems, and surface engineering.

Aiming at the problem of uneven preload of raised flange, a model considering elastic interaction is established to study the uniformity of bolt load under raised flange connection. Firstly, the elastic interaction coefficient method of bolt flange connection structure is extended to multiple times, and the initial preload of all bolts at each tightening step is determined by analytical model; secondly, a three-dimensional finite element model of bolt flange connection structure is established, and the correctness of the finite element model is verified by comparing with the literature test, and the influence of tightening sequence and tightening steps on bolt preload is explored; finally, the results of raised flange under symmetrical tightening conditions with and without considering elastic interaction are compared and analyzed. The results show that when the tightening method is symmetrical tightening and the tightening steps are three steps, the elastic interaction of each bolt is the smallest, and the final preload uniformity is better; on this basis, the results of the model considering elastic interaction further improve the uniformity of the final preload and are closer to the target preload. The problem of bolt preload dispersion caused by elastic interaction is solved, and the research results can provide guidance for the safety and reliability design of bolt flange connection structure.

The optical observation of sub-micron structures encounters significant challenges due to the inadequate spatial shape matching of optical devices and the limitations imposed by diffraction. These factors result in suboptimal imaging resolution and the introduction of defocus distortion. One approach to mitigating these limitations involves utilizing a liquid microlens (LML) assisted microscope, which offers real-time and localized control capabilities. However, the dynamic tuning of LML is subject to volatility and instability, adversely affecting submicron resolution imaging. To address this issue, a contour-following coating inspired by the natural cornea is applied to the droplet's surface, resulting in the formation of liquid microlens compound structures (LMLCS). Firstly, photoresist microholes are fabricated on the optical disc. Then, liquid self-assembly technology is used to fill glycerol in the microholes. At last, a conformal spreading method of UV-curable adhesive precursor film is developed to ensure the combination of the microlens bilayer structure. Through the application of optical information transmission theory, finite difference time domain (FDTD) method, and the principle of Abbe microscopic imaging, the imaging magnification process and sub-micron resolution characteristic of the LMLCS are revealed. Remarkably, the obtained focal width at half maximum (FWHM) of 1.54 mu m is smaller than the theoretical value, enabling reconfigurable sub-micron resolution observation and 1.63 times imaging magnification of 226 nm grating lines under the optical microscope. Compared with unencapsulated LML, this compound structure exhibits better sub-micron resolution capability, greater imaging magnification, and faster adaptive dynamic response characteristics. Consequently, the LMLCS introduces exciting prospects for exploring optical sub-micron measurement and sensing devices with exceptional performance.

Herein, a novel, highly efficient, and low -energy consumption four -stage ion -distillation of FCDI (ID-FCDI) device was developed that combined four commercial monovalent selective membranes with four flow electrode channels for high selectivity to extract Li + ions from salt lake. It exhibited excellent separation factor ( S Li + Mg 2+ = 11247.27), high enrichment ratio (4.95 times), super purity of Li + ion solution (99.97 %), and low molar energy consumption ( E m = 0.21 kWh mol -1 ) at mass ratio of Mg 2+ /Li + = 1:1. The mathematical model calculation revealed that the excellent selective separation effectiveness of the as -proposed ID-FCDI system is due to the unique property of the monovalent selectivity membranes, in which the transmembrane rate of lithium ions is ten times that of magnesium ions under the same condition. Furthermore, the separation mechanism of the asproposed ID-FCDI device for Li + /Mg 2+ ions was determined by the high electrosorption capacity of the flow electrode for Li + ions (1.14 times higher than Mg 2+ ions), low diffusion resistance (1.413 Omega), and high diffusion coefficient of Li + ions (2.83 times faster than Mg 2+ ions) by electrochemical measurement. On this basis, 3.92 times of lithium was successfully enriched in the natural salt lake brine of Golmud (mass ratio of Mg 2+ /Li + = 79.29), and the separation factor was 6307.17 with a 99.64 % purity of Li + ion solution and an E m of 0.20 kWh mol -1 . Finally, the Li 2 CO 3 product (99.66 %) was precipitated via the reaction between Na 2 CO 3 and the enriched Li + ion solution, consequently fulfilling the battery -level application of the industrial purity requirements. These findings highlight that this device is promising and profitable for lithium extraction from salt lake in industrial production.

A fretting wear model of a rough surface that conforms to the actual situation is established to accurately reveal the wear mechanism of the connection structure. In the ABAQUS software, the UMESHMOTION subroutine and the energy dissipation model are used to simulate the fretting wear of double rough surfaces. The new model, a single rough surface model, and a smooth model are compared to analyze their differences. In addition, the influence of surface roughness, material, and friction coefficient on the fretting wear of rough surfaces is systematically explored through finite element simulation. The results show that the model's reliability has been verified through Hertz's theory and experiments. The stress and wear of the contact surface are more realistically reflected by the double roughness model. Besides, with the increase of surface roughness and material rigidity and the decrease of friction coefficient, the wear of the double rough surface model becomes more severe. The research work provides a theoretical basis for the design and performance prediction of the connection structure.

The existing fault diagnosis methods for coal mine fully mechanized mining equipment lack systematic management and application of historical fault data of fully mechanized mining equipment. In response to this problem, knowledge graph technology is introduced to systematically manage the fault data of fully mechanized mining equipment. The top-down approach is used to construct the ontology of fully mechanized mining equipment fault knowledge. The knowledge of fully mechanized mining equipment fault is classified into four categories: fault location, fault phenomenon, fault cause, and treatment method. And the naming of the knowledge is standardized. The universal naming entity annotation method BIOES is used to manually annotate the fault knowledge of fully mechanized mining equipment. By combining bi-directional long short-term memory (BiLSTM) and conditional random field (CRF), the BiLSTM-CRF model is constructed. The marked fault knowledge of fully mechanized mining equipment is identified by the named entity, and the fault knowledge extraction is realized by manually extracting entity relationships. Combining the entity recognition results of the BiLSTM-CRF model with the manually extracted entity relationships, a Neo4j graph database is used to store the fault knowledge of fully mechanized mining equipment. A fault knowledge graph of fully mechanized mining equipment is constructed. The experimental results show that compared to the BiLSTM model and BiLSTM-Attention model, the acurracy of the BiLSTM-CRF model is significantly improved, reaching 87%. The F1 value also has a certain increase, reaching 69%. The construction of fully mechanized mining equipment fault knowledge graph can provide support for the effective analysis, management, and application of large-scale and multi-domain fully mechanized mining equipment fault data.

In view of the safety problem of the winch type Building maintenance unit,the ratchet and pawl type fall arrest device is designed. The key part of the research device is ratchet and pawl and establish dynamic mathematical model of ratchet. The relationship between the parameters that affect the braking performance of the ratchet and pawl type anti falling safety device is obtained;ADAMS is used to simulate the braking process of the device,and the braking performance curve of the device is obtained,and the test platform is built to test and verify the fall arrest device. The results show that the test data are basically consistent with the simulation data,which proves the accuracy of the simulation results. Moreover the fall arrest device braking is fast and reliable;Taking the length of the pawl XDEas the design variable,the optimal design is carried out by ADAMS. The simulation results show that the optimal length of the pawl of the fall arrest device is 115mm.

During the operation of 5-axis CNC machine tools, the angle deviation of the rotation axis greatly affects the tool path control. Therefore, a post-processing algorithm of five-axis CNC machine tool with optimized rotation angle was proposed. Firstly, the basic The structure of the machine tool was analyzed, the Hausdoff distance was used to obtain the matching error between the actual machined surface and the difference surface, and the error compensation method was used to compensate the obtained error, and the rotation axis angle of the machine tool was completed according to the processing result. Based on the optimization results, the machine tool coordinate system was established, and the machine tool post-processor was developed through the coordinate transformation results and integrated into the relevant software to realize the post-processing of the machine tool. The experimental results show that the proposed method achieves better processing results when used for post processing of machine tools.

In this study, a three-step strategy including electrochemical cathode deposition, self-oxidation, and hydrothermal reaction is applied to prepare the LiMn2 O4 nanosheets on carbon cloth (LMOns@CC) as a binder-free cathode in a hybrid capacitive deionization (CDI) cell for selectively extracting lithium from salt-lake brine. The binder-free LMOns@CC electrodes are constructed from dozens of 2D LiMn2 O4 nanosheets on carbon cloth substrates, resulting in a uniform 2D array of highly ordered nanosheets with hierarchical nanostructure. The charge/discharge process of the LMOns@CC electrode demonstrates that visible redox peaks and high pseudocapacitive contribution rates endow the LMOns@CC cathode with a maximum Li+ ion electrosorption capacity of 4.71 mmol g-1 at 1.2 V. Moreover, the LMOns@CC electrode performs outstanding cycling stability with a high-capacity retention rate of 97.4% and a manganese mass dissolution rate of 0.35% over ten absorption-desorption cycles. The density functional theory (DFT) theoretical calculations verify that the Li+ selectivity of the LMOns@CC electrode is attributed to the greater adsorption energy of Li+ ions than other ions. Finally, the selective extraction performance of Li+ ions in natural Tibet salt lake brine reveals that the LMOns@CC has selectivity ( α Mg 2 + Li + $\alpha _{{\mathrm{Mg}}^{2 + }}^{{\mathrm{Li}}^ + }$ = 7.48) and excellent cycling stability (100 cycles), which would make it a candidate electrode for lithium extraction from salt lakes.

针对螺栓结合面微观接触特性具有不确定性,传统基于确定性理论建立的模型难以完整表征结合面微观接触特性的问题,提出了一种基于蒙特卡洛法的螺栓结合面微观接触特性的不确定性量化方法.首先,基于分形理论,表征了同一粗糙度结合面微凸体轮廓高度,并采用矩谱法求解了结合面表面形貌参数区间;其次,利用中心极限定理,将表面形貌参数区间变为符合微凸体轮廓高度分布的高斯分布函数,解决了随机抽样误差的累加造成的置信水平降低;最后,将表面形貌参数的不确定性嵌入蒙特卡洛法,获得了结合面接触特性的区间估计,通过对比分析揭示了考虑不确定性因素时接触间隙对接触特性的影响规律.研究表明,表面形貌参数的不确定性对螺栓结合面接触特性变化具有显著影响,并导致不确定性的影响不断累加.该方法为准确量化螺栓结合面的不确定性提供了理论依据与参考.

Suction cups are widely utilized in industries and robotics fields for object manipulation and robotic positioning. Conventional vacuum pumps can remove fluid from the suction cup continuously, enabling reliable adhesion. However, the bulky and rigid nature limits their integration with soft robotics. On the other hand, suction cups driven by soft smart materials offer better integration with soft robots but face challenges in achieving continuous fluid removal, resulting in potential suction failures in case of seal breaks. Aiming to address this limitation, a novel self-loading suction cup driven by a resonant dielectric elastomer actuator is proposed. This mechanism allows for continuous and efficient removal of the enclosed fluid in the sucker, thereby achieving successful and sustained adhesion. The structure design is presented and its fundamental working principles are revealed through theoretical analysis and experiments. The effects of several key design parameters (i.e., actuation electric field amplitude, moving mass, substrate roughness) on the performance of the suction cup are experimentally characterized to achieve performance optimization. This design demonstrates a maximum net suction force of 24.9 N (12.7 kPa), which is equivalent to 80 times its body weight. The suction cup design holds potential application values in soft robots, surveillance and environmental monitoring.

For the purpose of service composition optimization of knowledge resources for complex parts in cloud manufacturing environment, a service composition optimization model with quality of service (QoS) as optimization objective is established. Firstly, gray relational analysis is used to preprocess manufacturing resources, reduce search range of knowledge resources and reduce search cost. Then, the improved ant colony algorithm is used to optimize the knowledge resources globally to improve matching speed. Finally, the ant colony back-propagation (BP) neural network algorithm is used to improve the learning efficiency and accuracy of knowledge service composition by optimizing the optimal solution in solution space again. The experimental results show that the usage of gray relational analysis, improved ant colony algorithm, and BP neural network can reduce the search time of knowledge service, improve the matching accuracy, and effectively solve the problem of knowledge service composition optimization.