High-steepness optical components are critical components of advanced optical systems such as lithography systems. In recent years, redundant degree-of-freedom (DOF) polishing systems based on industrial robots (IRs) integrated with external axes have been increasingly applied to the precision polishing of high-steepness optical components due to their advantages of high machining flexibility, large machining range and low cost. However, such systems often produce inconsistent and unsatisfactory surface form accuracy after polishing, due to inadequate pose matching and motion coordination between the robot and external axes. To address these issues, this paper proposes a globally optimized motion control method for redundant DOF robotic polishing systems based on the benchmark generatrix alignment (BGA) algorithm.Firstly, a multi-body kinematic model of the robot-turntable system is established, and comprehensive performance metrics for evaluating robot performance are defined, and a mapping relationship among the C-axis rotation angle, robot joint angles and robot comprehensive performance is constructed. On this basis, a BGA algorithm based on global pose optimization is proposed. By rotating the C-axis to align all machining points with the predefined benchmark generatrix, the comprehensive performance of the robot is improved globally, and the pose matching problem between the robot and C-axis is addressed. Furthermore, to overcome the inherent problem of limited dynamic response of the C-axis at the workpiece center, a zoned speed control strategy is integrated into the BGA algorithm framework. This strategy divides the machining area into normal-speed and low-speed zones, ensuring a stable feed rate while strictly satisfying the turntable mechanical constraints, thus mitigating the speed matching problem between the robot and C-axis. In addition, aiming at the surface segmentation problem, a quantitative calibration error mapping model is constructed to systematically analyze the influence law of each coordinate system error on machining quality. Theoretically, errors of the turntable and tool coordinate systems are identified as the primary causes of surface segmentation defects. Through precise coordinate system calibration and process optimization, surface segmentation defects and the resulting surface form accuracy degradation are substantially mitigated.Polishing experiments on planar and concave spherical optical components validate the effectiveness of the proposed method. After recalibration and process optimization, the surface peak-to-valley (PV) value of planar components is reduced from 0.346λ to 0.081λ, while the PV value of concave spherical components decreases from 0.272λ to 0.096λ without surface segmentation or over-polishing. This work provides a practical and generalizable solution for high-precision robotic polishing of complex optical components.

We report the friction behavior of graphene edges within a carbon film, which encompasses structures ranging from amorphous carbon (a-C) to graphene nanocrystalline carbon (GNC). Structural characterization revealed that vertically growing graphene nanocrystallites were implanted into the a-C structure, exposing high-density layer edges on the film surface. Atomic force microscopy (AFM) nanofriction tests highlighted the nature of graphene edge friction. Firstly, the edge friction of GNC films was tested in a critical-contact state, and the results showed that graphene edges exhibited lower friction forces than did a-C edges. Secondly, the surface friction of GNC films was investigated in a full-contact state, revealing that the edge friction of graphene nanocrystallites regulated the surface friction of GNC films. As the edge density of graphene nanocrystallites increased, the nanofriction force of GNC films decreased. Finally, the mechanism of the regulated friction behavior was attributed to the number of edges of the graphene nanocrystallites, which provided plentiful sp2 C dangling bonds with weak bonding interactions and edge quantum wells with low surface potentials for lowering friction. These findings shed light on the importance of graphene-related materials and their high-density edges in the structural design and nanofriction application of carbon films.

The gas film thickness in aerostatic bearings is on the micrometer scale, under which the gas flow within the film no longer follows conventional macroscopic behavior but instead falls within the micro-scale flow regime. Under microscale conditions, gas rarefaction effects cause the conventional no-slip boundary condition to break down, resulting in velocity slip at the gas-solid interface. This velocity slip alters the pressure distribution within the gas film, thereby affecting the performance of the bearing. To investigate the influence of velocity slip on the static and dynamic performance of aerostatic spindles, a gas film flow model incorporating the velocity-slip effect is established. The results indicate that velocity slip reduces the static load-carrying capacity of the bearing while increasing its static stiffness. In addition, velocity slip leads to a reduction in the dynamic coefficients and the natural frequencies of the spindle. Therefore, the effects induced by velocity slip cannot be neglected during the practical operation of aerostatic spindles. The present study reveals the variation of spindle bearing performance and provides a theoretical basis for the optimized design of aerostatic spindle bearings.

Reaction-bonded silicon carbide (RB-SiC) ceramics are highly hard and brittle materials with an inherently heterogeneous microstructure comprising SiC matrix, free silicon, and interfacial phases. Conventional finite element models based on homogeneous material assumptions are inadequate for capturing the complex deformation and removal mechanisms in RB-SiC machining. This study develops a two-dimensional single-grit cutting model incorporating zero-thickness cohesive elements to explicitly represent the multiphase structure (SiC matrix, free silicon and phase boundary) of the RB-SiC workpiece, which is validated through nano-scratch tests. Subsequently, the model is employed to investigate material removal mechanisms and cutting force characteristics of RB-SiC ceramics in elliptical ultrasonic vibration-assisted cutting (EUVAC). Simulation results identify three primary material removal modes: friction-induced plastic deformation, pulverization via pore–crack interaction, and brittle fracture dominated by interfacial debonding and crack propagation-with brittle fracture being the predominant mechanism. Cracks initiate preferentially within the low-toughness SiC matrix and propagate along phase boundaries. Furthermore, cutting forces are effectively suppressed around 40 kHz, with no substantial further reduction beyond this frequency. Increased cutting depth elevates cutting forces and accelerates the occurrence of peak forces due to enhanced material deformation resistance. Therefore, high-frequency and low-amplitude machining is recommended for RB-SiC ceramics. These findings provide a theoretical basis for high-quality processing of RB-SiC ceramics.

The development of alkaline-stable membranes is of paramount importance for the reliable operation of energy-related devices such as flow batteries and water electrolyzers. Intrinsically stable porous membranes are well suited for these devices, yet their integration into commercial-scale cell stacks has been seldom documented. Here, we report the pilot-scale fabrication of porous poly(ether sulfone) (PES)/ZrO2 mixed-matrix membranes, demonstrating efficient and stable performance in both an alkaline zinc-iron flow battery stack and a lab-scale zero-gap alkaline water electrolyzer stack. The obtained membrane shows only 0.218% mass degradation over 500 h in a 30 wt % KOH solution at 80°C and works efficiently for use in alkaline zinc-iron flow battery single cells, 500 W-scale cell stacks, and alkaline water electrolyzer stacks. This work propels energy-related devices from laboratory-scale development to industrial-scale stack operation, inspiring effective solutions for large-scale energy storage and conversion.

To address the challenge of time-varying characteristics of the tool influence function (TIF) and insufficient surface form convergence induced by dynamic factors such as polishing pad wear and polishing slurry concentration fluctuation in the ultra-precision polishing process, this paper proposes a process framework-level closed-loop optimization method based on online iterative correction of the TIF. Different from the traditional empirical trial-and-error mode that solely relies on the superposition of surface form residuals, the proposed method establishes a self-learning control framework with the capability of adaptive updating of underlying parameters. Its core lies in the introduction of an online dynamic correction algorithm based on residual minimization. Through least-squares inversion of the measured surface form error and dwell time data from the previous processing round, the comprehensive removal efficiency coefficient k of the Preston model is quantitatively solved and calibrated online. To verify the universality and robustness of the proposed method, multiple rounds of continuous single-run processing verification were conducted on circular fused silica elements (polished with a polyurethane small tool) and square glass-ceramic elements (processed via bonnet polishing), respectively. The experimental results show that, affected by dynamic time-varying disturbances, the prediction accuracy of the first-round surface form Peak-to-Valley (PV) value using the traditional static model is only 61.4

In the five-axis stable milling of thin-walled blades, significant variations in surface micro-ripple morphology have been observed under different tool rake and tilt angle combinations, even when cutting parameters are within the chatter-free zone. This phenomenon suggests that tool attitude regulates the formation of residual ripples through a dynamic mechanism rather than simple geometric projection. To reveal the physical nature of this process, this paper proposes a surface topography modeling method that couples the forced vibration response with tool attitude. First, a two-dimensional dynamic model considering periodic cutting force excitation is established to solve for the forced vibration responses in the feed and normal directions. These vibration perturbations are then superimposed onto the ideal tool path. Subsequently, a three-dimensional surface topography simulation model is constructed, incorporating coordinate transformations to predict the microscopic residual ripple structure under various attitude combinations. Simulation results based on two typical experimental attitudes (30 degrees/-45 degrees and 15 degrees/-15 degrees) show strong agreement with measured data in terms of residual height and corrugation patterns. The study reveals that the 30 degrees/-45 degrees attitude effectively suppresses normal vibration and improves surface quality. This work provides a theoretical basis and a predictive tool for tool attitude optimization and micro-quality control in complex multi-axis machining processes.

Reaction-bonded silicon carbide (RB-SiC) ceramics, characterized by high hardness, brittleness, and multiphase heterogeneity, exhibit severe surface/subsurface damage under dynamic machining loads. Due to the experimental cost constraints and the technical limitations in real-time monitoring of material removal mechanisms, this study addresses these challenges by developing a 3D single-abrasive-grit cutting finite element model (FEM) within ABAQUS/Explicit, integrating Voronoi tessellation and cohesive zone modelling (CZM) to approach RB-SiC’s heterogeneous microstructure. Scratch experiments validated the developed model’s accuracy in characterizing interfacial failure and material removal mechanisms. Subsequently, a preliminary investigation was conducted on the effect of cutting depth on cutting force for RB-SiC ceramics. This study offers foundational support for the future development of low-damage, high-quality machining strategies for RB-SiC ceramics, while simultaneously providing a referential framework for modelling and simulation of multiphase composite ceramics with analogous heterogeneous microstructures.

We reported the effect of doping on current-carrying friction behaviour of amorphous carbon (a-C) film. Si-doped, Ti-doped and pure a-C films were fabricated using a divergent electron cyclotron resonance plasma system. Structural analysis revealed that Si-doped and pure a-C films showed fully amorphous structures, while Ti-doped a-C films contained an amorphous matrix with embedded tiny sp(2) nanocrystallites. Current-carrying friction tests demonstrated Si-doped a-C films achieved low friction coefficients (mu = 0.02 similar to 0.04) within a short run-in period of 0 similar to 2 cycles. Similarly, Ti-doped a-C films reached low friction (mu = 0.03 similar to 0.05) within 2 similar to 3 cycles. In contrast, pure a-C films required longer run-in periods (10 similar to 15 cycles) to achieve low friction (mu = 0.05 similar to 0.06). Transfer film analysis identified Si-O/Si-OH bonds induced by Si-doping and sp(2) nanocrystallites facilitated by Ti-doping were critical factors to the rapid low-friction performance. Specifically, Si atoms contributed to terminal passivation of contact surfaces by reacting with oxygen to form Si-O/Si-OH bonds. While Ti atoms played a dual role in both inducing sp(2) nanocrystallites and promoting their formation under current-carrying conditions. The presence of terminal passivation and sp(2) nanocrystallites at contact interface enhanced the current-carrying friction performance of doped a-C films. These findings highlight the potential of Si- and Ti-doped a-C films in current-carrying friction applications.

Aerostatic spindle utilizes gas as the lubricating and supporting medium, enabling it to exhibit outstanding characteristics such as high precision, low temperature rise, and environmental friendliness during operation, thereby fulfilling the requirements of high-speed machining applications. However, during the operation of the aerostatic spindle, the increase in spindle speed induces velocity effects such as centrifugal force and gyroscopic effect, which jointly act on the spindle, resulting in vibration characteristics and rotational accuracy that differ from traditional conditions. Currently, there is relatively limited research on the influence of velocity effects on the motion behavior of aerostatic spindles. Therefore, in order to investigate the impact of velocity effects on the vibration characteristics and rotational accuracy of aerostatic spindles, a bearing-rotor system dynamics model based on velocity effects was established. The study found that velocity effects can increase the vibration amplitude and rotational errors of the spindle, and this influence intensifies as the spindle speed increases. Therefore, in the actual operation of the aerostatic spindle, the impact of the velocity effect cannot be neglected. This study provides an important theoretical basis for the dynamic prediction of the performance of the aerostatic spindle system and the optimal design of the spindle system.

The high hardness and low fracture toughness of reaction-bonded silicon carbide (RB-SiC) ceramics result in brittle fracture-dominated material removal during conventional grinding, inevitably inducing surface/subsurface damage that compromises component performance and longevity. To elucidate the nanoscale deformation behavior and material removal mechanisms of RB-SiC ceramics, this study employs nanoindentation and nano-scratch techniques with a Berkovich indenter, coupled with scanning electron microscopy (SEM), to systematically investigate its micromechanical properties and deformation characteristics. Nanoindentation results reveal average hardness (H) and elastic modulus (E) values of 35.03 GPa and 471.8 GPa, respectively. A pronounced indentation size effect is observed at depths < 500 nm, indicating that intrinsic material properties dominate machining outcomes at shallow machining depths, while their influence diminishes at greater depths. Nano-scratch analyses revealed the indenter's progressive penetration process and the transition in material removal mechanisms. Furthermore, the correlation between applied load and deformation mechanisms (plastic-to-brittle transition and crack nucleation/propagation from phase boundaries) and frictional responses (residual depth, tangential force) was systematically investigated. These findings provide mechanistic insights into the interplay between nanoscale mechanical behavior and machining-induced damage, establishing a theoretical framework for optimizing precision machining parameters to suppress brittle fracture and enhance surface integrity. The results bridge microscale mechanical responses to macroscale machining outcomes, offering foundational guidance for advancing the high-precision manufacturing of RB-SiC ceramic components.

In the engineering application of porous aerostatic radial bearings, the gas supplied is generally not absolutely clean, and the micropores in porous materials are usually very small. As the aerostatic bearing continues to operate, impurities in the gas gradually accumulate and form blockages at certain points in the porous medium, affecting the bearing performance. Therefore, a systematic study of the impact of blockages in porous media on aerostatic bearings is essential. This paper establishes lubrication models for porous aerostatic radial bearings under both blockage-free and blocked conditions. Through Fluent simulation analysis, the effects of the number, location, and size of blockages on the static characteristics of the bearing are examined. An analysis of the extent of impact of blockages on bearing performance is also conducted, providing a reference basis for judging the operating condition of the bearing and optimizing bearing design in practical applications.

Monocrystalline silicon, as a typical hard and brittle material, is prone to subsurface damage during nanomachining, which affects the subsequent performance of processed workpieces. By adjusting cutting parameters, subsurface damage and surface quality during the machining process can be controlled and improved. Therefore, this article explored the formation mechanism of sub-surface damage in monocrystalline silicon during nanocutting process through molecular dynamics(MD) simulation, and systematically studied the effects of four processing parameters, namely cutting crystal plane, cutting speed, tool rake angle, and relative tool sharpness, on the depth of sub-surface damage and surface roughness. The results indicated that high hydrostatic stress and high temperature during the cutting process promote the amorphous phase transition of the workpiece atoms, resulting in subsurface damage. Changes in cutting parameters can significantly affect subsurface damage and surface roughness. By optimizing cutting parameters, subsurface damage and surface roughness can be effectively controlled and improved.

Under the pressures of climate change and energy crises, the manufacturing industry faces significant challenges, especially with stringent carbon emission regulations. This paper addresses optimizing energy consumption in high-precision machine tools, specifically during the grinding phase of wafer grinders. Traditional theoretical models fail to accurately predict energy consumption due to the varied energy types and complex flows in wafer grinders. To tackle this, we propose a combined theoretical and experimental mathematical modeling approach. We constructed a model with unknown coefficients, which were calibrated using experimental grinding data, allowing accurate power consumption predictions during fine grinding. We optimized grinding power consumption, material removal rate, and surface roughness using wheel speed, wafer speed, and feed rate as decision variables through a multi-objective genetic algorithm. Experimentally validated, our model enhances energy efficiency, offering theoretical support for sustainable manufacturing.

In the field of microscale ultra-precision machining, the radial vibration of aerostatic spindles is a key factor affecting machining accuracy, and the role of process damping in regulating this vibration remains to be systematically clarified. This research focuses on exploring how process damping influences the radial vibration of aerostatic spindles under such cutting conditions, establishing a corresponding dynamic model, and verifying the model’s accuracy. Methodologically, an interference volume identification model is built based on the tool-workpiece indentation effect to calculate process damping, the LFR model is used to modify the unsteady Reynolds equation and integrate process damping into the aerostatic spindle dynamics model, followed by simulation of the spindle’s dynamic characteristics at 1000–4000 r/min conducted via MATLAB’s ODE45 function, and experimental verification is performed using a bidirectional dynamic measurement system to collect radial vibration data. The results show that process damping significantly suppresses radial translational vibrations at low speeds, with the suppression effect weakening as speed increases; it has little impact on radial angular displacements. It is concluded that process damping effectively suppresses the low-speed radial translational vibration of the aerostatic spindle, and the established model is consistent with actual machining, providing a basis for spindle vibration control in ultra-precision machining.

This study investigates the formation mechanisms and suppression strategies for common metallurgical defects—including balling, porosity, powder adhesion, and spatter—in Ti6Al4V alloys fabricated by selective laser melting (SLM). A comprehensive numerical framework linking process parameters, molten pool dynamics, and defect evolution is developed to optimize linear and volumetric energy densities. This research further proposes actionable guidelines aimed at achieving high-quality manufacturing outcomes in SLM processes. A three-dimensional transient thermal-fluid flow model for SLM-processed Ti6Al4V powder was established to elucidate molten pool dynamics under controlled processing conditions. Utilizing dimensionless numbers to quantify dynamic behaviors, the simulations were optimized to accurately capture molten pool evolution. Defect formation mechanisms, particularly for balling and porosity, were thoroughly examined through integrated numerical modeling and experimental validation, emphasizing the critical impact of linear and volumetric energy densities. The analysis revealed that thermal convection predominantly governs heat transfer within the molten pool, driven primarily by evaporation recoil pressure, surface tension, and Marangoni shear stress. A reduction in energy density adversely affects molten pool fluidity, promoting porosity formation as molten metal solidifies into spherical shapes driven by surface tension. This resultant porosity significantly deteriorates the mechanical properties of fabricated parts, underscoring the necessity for meticulous control over energy density. Optimizing key processing parameters, such as laser power, scanning speed, and scanning spacing, enables the formation of high-quality components. The proposed “process parameters‒molten pool characteristics‒forming quality” analytical framework provides robust guidance for parameter optimization. Application of this framework effectively mitigates metallurgical defects, thereby enhancing the density and mechanical performance of parts manufactured through SLM.

This paper proposes an improved terminal sliding mode control impedance control method (ISMIC) for manipulators to keep the contact force constant during machining. Firstly, environmental uncertainties and impedance model uncertainties are introduced into the traditional impedance model to derive an impedance model that includes the effects of uncertainties factors and estimation of uncertainty using a disturbance observer. Then, a finite-time convergence controller is designed with a terminal sliding mode surface, and an improved reaching law is chosen. Finally, the machining process is simulated with the proposed controller and compared with previous literature. Simulation results show that the control method proposed in this paper improves the convergence accuracy by more than 93.6% compared to similar control methods in the literature. This method can greatly improve the accuracy of force control and has the potential for practical application.

An acoustic signal acquisition experiment platform was constructed to gather the acoustic signals throughout the formation of 35 single-tracks of a 120 mm length copper-tin alloy in order to monitor and precisely manage the selective laser melting (SLM) forming process and enhance overall quality. The monitoring of the SLM forming process includes the analysis of the time and frequency domains, the extraction of the SLM process features using linear prediction techniques, and the development of support vector machine (SVM) model, back-propagation (BP) neural network models, and convolutional neural network models. The results show that the over-melted state can be identified by extracting time and frequency-domain features over a given range, but the normal and unmelted states are difficult to distinguish. The convolutional neural network model had a recognition rate of 99%, the BP neural network had an effective recognition rate of 90%, and the SVM model had a combined classification rate of 83.14% for the three states after optimization. In contrast, the convolutional neural network model performs best in monitoring and offers a framework and point of reference for acoustic signal analysis and online SLM quality monitoring.

Single crystal 3C-SiC as a typical hard and brittle material. Due to its extremely high hardness and brittleness, diamond tools can have drastic wear problems in a short period of time when performing nano-machining processes. Single-crystal 3C-SiC has good anisotropy. In order to gain insight into the effect of crystal orientation on the tool wear behavior during nano-cutting, this paper investigated the tool wear for six cutting crystals downward using molecular dynamics simulation. The tool wear process was analyzed in terms of tool wear, cutting stress, cutting temperature, cutting force and subsurface damage. The results showed that the tool is subjected to large hydrostatic stress concentrations, radial forces and tool temperature concentrations during machining. Under these effects, abrasive wear, graphitic wear and amorphous phase change were produced. In addition, the clearance face of the tool is more severely worn than the rake face. By reasonably selecting the cutting crystal orientation, well control and improvement of tool wear and machined surface quality can be achieved.

Nickel alloys are widely used in aerospace and defense industries due to their excellent high-temperature characteristics. In order to increase fatigue resistance and oxidation corrosion resistance, aerospace curved surface parts always need to be strengthened using laser shock peening technology on five-axis machines. However, it is easy to generate dense laser spots in certain regions during the process, which might lead to an uneven distribution of residual stress on surfaces of the component. To address this issue, the study investigates the problem of excessive spot overlap rate at large curvature positions caused by a mismatch between the surface curvature of curved components and the dynamic performance of equipment, then proposes an optimization algorithm for motion control based on linear interpolation principles. By analyzing the relationship between the feed rate of the program segment and the actual feed rate, the speed of each axis of the machine tool is confirmed and adjusted, and the control flow of the optimization algorithm is established. The feasibility of the algorithm is preliminarily verified by comparing the changing trend of feed rate before and after optimization through simulation. In actual laser shock peening experiments on nickel alloy spherical shell elements, the laser spot distribution on the surface of the component is uniform, and the relative error range between the actual overlap ratio and the theoretical value can be controlled within 5