Additive manufacturing, particularly 3D printing, has revolutionized the manufacturing industry by allowing the production of complex and intricate parts at a lower cost and with greater efficiency. However, 3D-printed parts frequently require post-processing or integration with other machining technologies to achieve the desired surface finish, accuracy, and mechanical properties. Ultra-precision machining (UPM) is a potential machining technology that addresses these challenges by enabling high surface quality, accuracy, and repeatability in 3D-printed components. This study provides an overview of the current state of UPM for 3D printing, including the current UPM and 3D printing stages, and the application of UPM to 3D printing. Following the presentation of current stage perspectives, this study presents a detailed discussion of the benefits of combining UPM with 3D printing and the opportunities for leveraging UPM on 3D printing or supporting each other. In particular, future opportunities focus on cutting tools manufactured via 3D printing for UPM, UPM of 3D-printed components for real-world applications, and post-machining of 3D-printed components. Finally, future prospects for integrating the two advanced manufacturing technologies into potential industries are discussed. This study concludes that UPM is a promising technology for 3D-printed components, exhibiting the potential to improve the functionality and performance of 3D-printed products in various applications. It also discusses how UPM and 3D printing can complement each other.

The complexity of aeroengine external piping systems necessitates the implementation of automated design processes to reduce the duration of the design cycle. However, existing routing algorithms often fail to meet designer requirements because of the limitations in providing a single solution and the inadequate consideration for route constraints. In this study, we propose the multi-solution pipe-routing method for aeroengines. This method utilizes a hybrid encoding approach by incorporating fixed-length encoding to represent route constraints and variable-length encoding and indicate free-exploration points. This approach enables designers to specify route constraints and iterate over the appropriate number of control points by employing a modified genetic iteration mechanism for variable-length encoding. Furthermore, we employ a pipe-shaped clustering niche method to enhance result diversity. The practicability of the newly proposed method is confirmed through comparative experiments and simulations based on the “AeroPiping” system developed on Siemens NX. Typical solutions demonstrate significant differences in circumferential and axial orientations while still satisfying engineering constraints.

This study proposes an adaptive control strategy for unmanned mining shovel digging trajectory tracking based on radial basis function neural network (RBFNN) and a class of unmanned mining shovel time-varying systems with model uncertainty and external disturbances. A new set of Lagrangian dynamics differential equations is reconstructed by utilizing the kinematic model of the electric shovel and considering external disturbances along with modeling uncertainties. This approach lays the groundwork for subsequent adaptive controllers. The proposed controller is designed to regulate the position errors of the unmanned mining electric shovel system, which is characterized by a complex structure, high load, large size, and strong coupling. It takes the deviation values and their derivatives of the lifting and pushing movements as inputs and adjusts the output torque to converge the bucket position to the desired trajectory. The controller utilizes the RBFNN in the control law to compensate for uncertainties in this type of system with large disturbances and inertia. This compensation helps eliminate the impact of external disturbances and modeling uncertainties on the unmanned mining electric shovel’s ability to follow the excavation trajectory. The consistent boundedness of the closed-loop system’s ultimate limits is proven through Lyapunov stability theory. Finally, the effectiveness of the proposed solution is validated through simulation experiments.

This study proposes a data-driven friction modeling and compensation method aimed at solving the problem of servo performance degradation caused by friction in rotary servo actuators. First, a data-driven friction modeling method is proposed on the basis of the physics-informed neural network (PINN) and the LuGre model. The constructed friction model consists of sliding regime, static regime, and presliding regime, which extends the variables of the friction model to include velocity and position. The data-driven friction model not only retains the accuracy of the LuGre model in describing the dynamic behavior of friction at zero velocity but also improves the accuracy and convergence speed of the model through the powerful learning ability of PINN, which is verified in the two examples of constructing friction test data. Second, on the basis of the data-driven friction model, a composite compensation strategy centered on friction compensation is proposed. The friction compensator is used to compensate the internal friction of the actuator, and the extended Kalman filter is used to suppress the random disturbance to achieve the precise control of the servo actuator. Experimental validation of the proposed compensation strategy against three traditional control methods demonstrates its superiority, with average improvements of 49.5%, 30.4%, and 32.7% in velocity tracking accuracy, respectively, while ensuring consistent accuracy across different positions. The proposed data-driven friction modeling and compensation method provides a new perspective and method for overcoming the effect of friction.

Planet pin position errors significantly affect the mechanical behavior of planetary transmissions at both the power-sharing level and the gear tooth meshing level, and its tolerance properties are one of the key design elements that determine the fatigue reliability of large aviation planetary systems. The dangerous stress response of planetary systems with error excitation is analyzed according to the hybrid finite element method, and the weakening mechanism of large-size carrier flexibility to this error excitation is also analyzed. In the simulation and analysis process, the Monte Carlo method was combined to take into account the randomness of planet pin position errors and the coupling mechanism among the error individuals, which provides effective load input information for the fatigue reliability evaluation model of planetary systems. In addition, a simulation test of gear teeth bending fatigue intensity was conducted using a power flow enclosed gear rotational tester, providing the corresponding intensity input information for the reliability model. Finally, under the framework of stress-intensity interference theory, the computational logic of total formula is extended to establish a fatigue reliability evaluation model of planetary systems that can simultaneously consider the failure correlation and load bearing time-sequence properties of potential failure units, and the mathematical mapping of planet pin positional tolerance to planetary systems fatigue reliability was developed based on this model. Accordingly, the upper limit of planet pin positional tolerance zone can be determined at the early design stage according to the specific reliability index requirements, thus maximizing the balance between reliability and economy.

Hybrid deposition with microrolling is a promising arc-based direct energy deposition technique to rapidly build complex parts, whose performance is comparable to that of their wrought counterparts. Complex forming conditions and bead morphologies pose difficulties in controlling the morphologies from a single weld bead to built part profiles, and these difficulties hinder the widespread application of the technique. Here, a model that can automatically generate optimal process parameters on the basis of the infrared image, including thermal information and the point cloud information of the target weld bead, is developed. Results show that the errors in critical parameters, namely, feed speed, travel speed, and rolling force, are below 0.4%, 0.9%, and 2%, respectively, indicating that the proposed technique outperforms the compared methods. Furthermore, validation reveals that the actual depositing bead is similar (deviation below 0.05 mm) to the target bead. The proposed strategy provides an effective foundation for dynamic path planning and can considerably improve printing efficiency and precision.

Simulation model optimization plays a crucial role in the accurate prediction of material removal function in bonnet polishing processes, but model complexity often poses challenges to the practical implementation and efficiency of these processes. This paper presents an innovative method for optimizing simulation model parameters, focusing on achieving consistent contact area and the accurate prediction of the material removal function while preventing increase in model complexity. First, controllable and uncontrollable factors in bonnet simulations are analyzed, and then a simplified contact model is developed and applied under constant force conditions. To characterize the bonnet’s contact performance, a contact area response curve is introduced, which can be obtained through a series of single spot contact experiments. Furthermore, a rubber hyperelastic parameter optimization model based on a neural network is proposed to achieve optimal matching of the contact area between simulation and experiment. The average deviation of the contact area under different conditions was reduced from 22.78% before optimization to 3.43% after optimization, preliminarily proving the effectiveness of the proposed simulation optimization model. Additionally, orthogonal experiments are further conducted to validate the proposed approach. The comparison between the experimental and predicted material removal functions reveals a high consistency, validating the accuracy and effectiveness of the proposed optimization method based on consistent contact response. This research provides valuable insights into enhancing the reliability and effectiveness of bonnet polishing simulations with a simple and practical approach while mitigating the complexity of the model.

With the continued shrinking of the critical dimensions (CDs) of wafer patterning, the requirements for modeling precision in optical proximity correction (OPC) increase accordingly. This requirement extends beyond CD controlling accuracy to include pattern alignment accuracy because misalignment can lead to considerable overlay and metal-via coverage issues at advanced nodes, affecting process window and yield. This paper proposes an efficient OPC modeling approach that prioritizes pattern-shift-related elements to tackle the issue accurately. Our method integrates careful measurement selection, the implementation of pattern-shift-aware structures in design, and the manipulation of the cost function during model tuning to establish a robust model. Confirmatory experiments are performed on a via layer fabricated using a negative tone development. Results demonstrate that pattern shifts can be constrained within a range of ±1 nm, remarkably better than the original range of ±3 nm. Furthermore, simulations reveal notable differences between post OPC and original masks when considering pattern shifts at locations sensitive to this phenomenon. Experimental validation confirms the accuracy of the proposed modeling approach, and a firm consistency is observed between the simulation results and experimental data obtained from actual design structures.

End worm gear drives are characterized by their multi-tooth contact, compact contour, and theoretical potential to overcome some inherent flaws of cylindrical worm drives. However, quantitative basic research on end worm gear drives is currently inadequate, which hinders the development of this transmission. This work focuses on the computational design of end worm gear drives and proposes a new Niemann-type design. Meshing models of the proposed drive are established, and its engagement theory is deduced systematically. Based on the derived tooth surface equations, an innovative research methodology for the tooth curve geometry of the end worm gear is created, and the tooth curve in the worm gear reference plane is proved to be a spiral. An improved formula for the lubrication angle is developed, which can provide more rational numerical results for the angle. Theoretically, the modified formula is universally applicable for line contact drives and can be used to quantitatively investigate the lubrication level between the teeth for the proposed drive. Simulation outcomes demonstrate the favorable characteristics of the transmission, including broad conjugate areas, even contact lines, and fine global lubrication state.

Recently, intelligent fault diagnosis methods have been employed in the condition monitoring of rotating machinery. Among them, graph neural networks are emerging as a new feature extraction tool that can mine the relationship characteristics between samples. However, many existing graph construction methods suffer from structural redundancy or missing node relationships, thus limiting the diagnosis accuracy of the models in practice. In this paper, an adaptive adjustment k-nearest neighbor graph-driven dynamic-weighted graph attention network (AAKNN-DWGAT) is proposed to address this problem. First, time-domain signals are transformed into frequency-domain features by using fast Fourier transformation. Subsequently, a frequency similarity evaluation method based on dynamic frequency warping is proposed, which enables the conversion of distance measurements into a frequency similarity matrix (FSM). Then, an adaptive edge construction operation is conducted on the basis of FSM, whereby the effective domain is captured for each node using an adaptive edge adjustment method, generating an AAKNN graph (AAKNNG). Next, the constructed AAKNNG is fed into a dynamic-weighted graph attention network (DWGAT) to extract the fault features of nodes layer by layer. In particular, the proposed DWGAT employs a dynamic-weighted strategy that can update the edge weight periodically using high-level output features, thereby eliminating the adverse impacts caused by noisy signals. Finally, the model outputs fault diagnosis results through a softmax classifier. Two case studies verified the effectiveness and the superiority of the proposed method compared with other graph neural networks and graph construction methods.

The primary mode of extraterrestrial exploration is a robotic system comprising a lander and a rover. However, the lander is immovable, and the rover has a restrictive detection area because of the difficulties of reaching complex terrains, such as those with deep craters. In this study, a six-legged mobile repetitive lander with landing and walking functions is designed to solve these problems. First, a six-legged mobile repetitive lander and its structure are introduced. Then, a soft-landing method based on compliance control and optimal force control is addressed to control the landing process. Finally, the experiments are conducted to validate the soft-landing method and its performances. Results show that the soft-landing method for the six-legged mobile repetitive lander can successfully control the joint torques and solve the soft-landing problem on complex terrains, such as those with steps and slopes.

Laser cleaning technology has emerged as a rapidly developing high-tech surface engineering technology in recent years. It is considered the most promising green cleaning technology in the 21st century, and related patent applications exhibit an explosive growth trend. This study summarizes the overall trend of patent applications in laser cleaning in China, with a focus on systematically analyzing patents related to cleaning different substrates and coatings, cleaning equipment, and processes over the past 6 years. The main characteristics of the growth trend, institutional attributes, regional distribution, and type proportion of laser cleaning patent applications in China are clarified. The patent application characteristics of laser cleaning for different substrate materials and coatings are identified. The progress in research and development of laser cleaning equipment, cleaning terminals, and monitoring devices is outlined, while potential for maintaining substrate surface integrity and achieving surface functionalization through laser cleaning is explored. Areas for improvement in laser cleaning are determined to support the innovative development of laser cleaning technology.

High-performance carbon fiber-reinforced polyether-ether-ketone (CF/PEEK) has been gradually applied in aerospace and automobile applications because of its high strength-to-weight ratio and impact resistance. The dry-machining requirement tends to cause the cutting temperature to surpass the glass transition temperature (T_g), leading to poor surface quality, which is the bottleneck for dry milling of CF/PEEK. Temperature suppression has become an important breakthrough in the feasibility of high-speed dry (HSD) milling of CF/PEEK. However, heat partitioning and jet heat transfer mechanisms pose strong challenges for temperature suppression analytical modeling. To address this gap, an innovative temperature suppression analytical model based on heat partitioning and jet heat transfer mechanisms is first developed for suppressing workpiece temperature via the first-time implementation of an air jet cooling process in the HSD milling of UD-CF/PEEK. Then, verification experiments of the HSD milling of UD-CF/PEEK with four fiber orientations are performed for dry and air jet cooling conditions. The chip morphologies are characterized to reveal the formation mechanism and heat-carrying capacity of the chip. The milling force model can obtain the force coefficients and the total cutting heat. The workpiece temperature increase model is validated to elucidate the machined surface temperature evolution and heat partition characteristics. On this basis, an analytical model is verified to predict the workpiece temperature of air jet cooling HSD milled with UD-CF/PEEK with a prediction accuracy greater than 90%. Compared with those under dry conditions, the machined surface temperatures for the four fiber orientations decreased by 30%–50% and were suppressed within the T_g range under air jet cooling conditions, resulting in better surface quality. This work describes a feasible process for the HSD milling of CF/PEEK.

In today’s society, parallel manipulators (PMs) are widely used in industrial production, aerospace, and other fields, where their forward kinematic analyses often serve as the foundation for various tasks, such as design, calibration, and control. In the past few decades, this issue has seemingly been repeatedly addressed using various numerical methods, intelligent algorithms, and algebraic tools. While it is undeniable that solving the equations is easier with current technology, the problem of “how to formulate solvable equations” is often overlooked. This analysis issue typically involves establishing non-linear, multi-parameter, high-dimensional, and strong-coupled mathematical equations, which, from a geometric perspective, is also considered a process of solving a spatial polyhedron. When considering the temporal dimension of motion between two isomorphic polytopes, based on calculus theory, it has been found that this non-linear problem can be transformed into the superposition of multiple iteratively linear equations. Consequently, we propose an original method for the forward kinematic analysis of PMs, namely the finite-step-integration (FSI) method. In this study, the mathematical principles and modeling methods of the FSI method are elucidated, and the modeling and programming processes of the FSI method are demonstrated using general 6-UPS and 3-UPS/S manipulators as examples. Through the analysis of its unique algebraic structure, the methods for singularity determination and motion tracking characteristic analysis are investigated. This method addresses the long-standing challenges in the forward kinematic modeling of PMs, which is applicable for design, calibration, and control, while also offering novel insights for modeling and solving certain non-linear engineering problems.

Excess materials are left inside aircraft wings due to manual operation errors, and the removal of excess materials is very crucial. To increase removal efficiency, a continuum robot (CR) with a removal end-effector and a stereo camera is used to remove excess objects. The size and weight characteristics of excess materials in aircraft wings are analyzed. A novel negative pressure end-effector and a two-finger gripper are designed based on the CR. The negative pressure end-effector aims to remove nuts, small rivets, and small volumes of aluminum shavings. A two-finger gripper is designed to remove large volumes of aluminum shavings. A stereo camera is used to achieve automatic detection and localization of excess materials. Due to poor lighting conditions in the aircraft wing compartment, supplementary lighting devices are used to improve environmental lighting. Then, You Only Look Once (YOLO) v5 is used to classify and detect excess objects, and two training data sets of excess objects in two wings are constructed. Due to the limited texture features inside the aircraft wings, this paper adopts an image-matching method based on the results of YOLO v5 detection. This matching method avoids the performance instability problem based on Oriented Fast and Rotated BRIEF feature point matching. Experimental verification reveals that the detection accuracy of each type of excess exceeds 90%, and the visual localization error is less than 2 mm for four types of excess objects. Results show the two end-effectors can work well for the task of removing excess material from the aircraft wings using a CR.

Photoacoustic detection has shown excellent performance in measuring thickness and detecting defects in metal nanofilms. However, existing research on ultrafast lasers mainly focuses on using picosecond or nanosecond lasers for large-scale material processing and measurement. The theoretical study of femtosecond laser sources for photoacoustic nondestructive testing (NDT) in nanoscale thin film materials receives much less emphasis, leading to a lack of a complete physical model that covers the entire process from excitation to measurement. In this study, we developed a comprehensive physical model that combines the two-temperature model with the acoustic wave generation and detection model. On the basis of the physical model, we established a simulation model to visualize the ultrafast laser-material interaction process. The damage threshold of the laser source is determined, and the effect of key parameters (laser fluence, pulse duration, and wavelength) for AlCu nanofilms on the femtosecond photoacoustic NDT process is discussed using numerical results from the finite element model. The numerical results under certain parameters show good agreement with the experimental results.

Multiscale structures require excellent multiphysical properties to withstand the loads in various complex engineering fields. In this study, a concurrent isogeometric topology optimization method is proposed to design multiscale structures with high thermal conductivity and low mechanical compliance. First, the mathematical description model of multi-objective topology optimization for multiscale structures is constructed, and a single-objective concurrent isogeometric topology optimization formulation for mechanical and thermal compliance is proposed. Then, by combining the isogeometric analysis method, the material interpolation model and decoupled sensitivity analysis scheme of the objective function are established on macro and micro scales. The solid isotropic material with penalization method is employed to update iteratively the macro and microstructure topologies simultaneously. Finally, the feasibility and advantages of the proposed approach are illustrated by several 2D and 3D numerical examples with different volume fractions, while the effects of volume fraction and different boundary conditions on the final configuration and multi-objective performance of the multiscale structure are explored. Results show that the isogeometric concurrent design of multiscale structures through multi-objective optimization can produce better multi-objective performance compared with a single-scale one.

In ocean exploration, underwater hydraulic manipulators (UHMs) driven by water hydraulics may become favored over oil-based systems because of their eco-friendliness and ability for continuous operation. A water hydraulic high-speed on/off valve (WHSV), with good sealing and fast response, may be used as a core control component of UHM. The comprehensive performance of the WHSV needs to be improved to enhance the accuracy, continuity, and reliability of UHM. In this study, the relationship between the negative voltage and the WHSV characteristics, including dynamic performance, power losses, and impact performance, is studied by finite element simulation. Furthermore, a multi-objective optimization method is proposed to improve the comprehensive performance of the WHSV. This method integrates the optimal Latin hypercube sampling method, universal Kriging surrogate model, non-dominated sorting genetic algorithm II, and Technique for Order Preference by Similarity to Ideal Solution methods to optimize the equivalent amplitude and duration of the negative voltage. Our findings reveal that the closing time decreases with the increase in the equivalent amplitude and duration of the negative voltage, while the opposite is observed in the power losses and maximum impact equivalent stress of the valve seat. Optimization results show a slight 3.3% increase in closing time of the WHSV but significant reductions in total power loss (9.8%), maximum impact equivalent stress (14.5%), and maximum total deformation (19.8%). This study provides a practical optimization approach for enhancing the comprehensive performance of the WHSV for improved UHM operation.

The optimization of two-scale structures can adapt to the different needs of materials in various regions by reasonably arranging different microstructures at the macro scale, thereby considerably improving structural performance. Here, a multiple variable cutting (M-VCUT) level set-based data-driven model of microstructures is presented, and a method based on this model is proposed for the optimal design of two-scale structures. The geometry of the microstructure is described using the M-VCUT level set method, and the effective mechanical properties of microstructures are computed by the homogenization method. Then, a database of microstructures containing their geometric and mechanical parameters is constructed. The two sets of parameters are adopted as input and output datasets, and a mapping relationship between the two datasets is established to build the data-driven model of microstructures. During the optimization of two-scale structures, the data-driven model is used for macroscale finite element and sensitivity analyses. The efficiency of the analysis and optimization of two-scale structures is improved because the computational costs of invoking such a data-driven model are much smaller than those of homogenization.

Surface thermal damage in a difficult-to-process metal precision grinding workpiece has emerged as a technical bottleneck restricting machining quality. As an alternative to traditional pouring cooling, a green clean minimum-quantity lubrication technology still has defects, such as insufficient heat dissipation. The use of cryogenic air instead of normal temperature air, that is, the supply of low-temperature energized lubricant, can effectively improve oil film heat transfer and lubrication performance in a grinding area. Under the premise of ensuring the effective flow of lubricating oil in a grinding zone, the thickness of a liquid film in the wedge zone of a grinding wheel or workpiece is the key factor for determining its performance. However, the dynamic mechanism of droplet formation and distribution of liquid film thickness are still unclear. Hence, the mechanism by which nozzle orientation influences the effective region of a liquid film was analyzed, and the range of nozzle inclination that helps to atomize droplets and enables them to enter the grinding zone was revealed. Then, the dynamic mechanism of atomized droplet film formation was analyzed, and the influence of normal and tangential momentum sources generated by gas impingement perturbation flow and droplet impingement steady flow on the driving effect of liquid film flow was revealed. The thickness distribution model of a liquid film in the impact zone of gas–liquid two-phase flow under different cryogenic air temperatures was established. The model results under different working conditions were obtained by numerical analysis, and validation experiments were carried out. Results show that the measured values agree with the theoretical values. At 0.4 MPa air pressure, the thickness of the liquid film in the impact zone of the atomized droplets increases with decreasing cryogenic air temperature. At −10 and −50 °C, the thickness of the liquid film is 0.92 and 1.26 mm, respectively. Further, on the basis of the surface topography model of cubic boron nitride grinding wheel, the pose relationship of any three adjacent abrasive particles was analyzed, and the theoretical model of abrasive clearance volume was established. The dynamic variation of abrasive clearance volume distribution domain is [70.46, 78.72] mm³, and the total volume distribution domain is [140.84, 155.67] mm³. The research will provide a theoretical basis for the application of cryogenic air minimum quantity lubrication technology to hard metal grinding.

Electrostatic atomization minimum quantity lubrication (EMQL) employs the synergistic effect of multiple physical fields to atomize minute quantities of lubricant. This innovative methodology is distinguished by its capacity to ameliorate the atomization attributes of the lubricant substantially, which subsequently augments the migratory and infiltration proficiency of the droplets within the complex and demanding milieu of the cutting zone. Compared with the traditional minimum quantity lubrication (MQL), the EMQL process is further complicated by the multiphysical field influences. The presence of multiple physical fields not only increases the complexity of the forces acting on the liquid film but also induces changes in the physical properties of the lubricant itself, thus making the analysis of atomization characteristics and energy distribution particularly challenging. To address this objective reality, the current study has conducted a meticulous measurement of the volume average diameter, size distribution span, and the percentage concentration of inhalable particles of the charged droplets at various intercept positions of the EMQL nozzle. A predictive model for the volume-averaged droplet size at the far end of the EMQL nozzle was established with the observed statistical value F of 825.2125, which indicates a high regression accuracy of the model. Furthermore, based on the changes in the potential energy of surface tension, the loss of kinetic energy of gas, and the electric field work at different nozzle orifice positions in the EMQL system, an energy distribution ratio model for EMQL was developed. The energy distribution ratio coefficients under operating conditions of 0.1 MPa air pressure and 0 to 40 kV voltage on the 20 mm cross-section ranged from 3.094‰ to 3.458‰, while all other operating conditions and cross-sections had energy distribution ratios below 2.06‰. This research is expected to act as a catalyst for the progression of EMQL by stimulating innovation in the sphere of precision manufacturing, providing theoretical foundations, and offering practical guidance for the further development of EMQL technology.

The nickel-based high-temperature alloy GH4169 is the material of choice for manufacturing critical components in aeroengines, and electrostatic atomization minimum quantity lubrication (EMQL) milling represents a fundamental machining process for GH4169. However, the effects of electric field parameters, jet parameters, nozzle position, and milling parameters on milling performance remain unclear, which constrains the broad application of EMQL in aerospace manufacturing. This study evaluated the milling performance of EMQL on nickel-based alloys using soybean oil as the lubrication medium. Results revealed that compared with conventional pneumatic atomization MQL milling, EMQL reduced the milling force by 15.2%–15.9%, lowered the surface roughness by 30.9%–54.2%, decreased the average roughness spacing by 47.4%–58.3%, and decreased the coefficient of friction and the specific energy of cutting by 55% and 19.6%, respectively. Subsequent optimization experiments using orthogonal arrays demonstrated that air pressure most significantly affected the milling force and specific energy of cutting, with a contribution rate of 22%, whereas voltage had the greatest effect on workpiece surface roughness, contributing 36.71%. Considering the workpiece surface morphology and the potential impact of droplet drift on environmental and health safety, the optimal parameter combination identified were a flow rate of 80 mL/h, an air pressure of 0.1 MPa, a voltage of 30 kV, a nozzle incidence angle of 35°, an elevation angle of 30°, and a target distance of 40 mm. This research aimed to provide technical insights for improving the surface integrity of aerospace materials that are difficult to machine during cutting operations.

The design of single-degree-of-freedom spatial mechanisms tracing a given path is challenging due to the highly non-linear relationships between coupler curves and mechanism parameters. This work introduces an innovative application of deep learning to the spatial path synthesis of one-degree-of-freedom spatial revolute–spherical–cylindrical–revolute (RSCR) mechanisms, aiming to find the non-linear mapping between coupler curve and mechanism parameters and generate diverse solutions to the path synthesis problem. Several deep learning models are explored, including multi-layer perceptron (MLP), variational autoencoder (VAE) plus MLP, and a novel model using conditional-\beta -\text{VAE} (\text{c}-\beta -\text{VAE}). We found that the \text{c}- \beta -\text{VAE} model with β = 10 achieves superior performance by predicting multiple mechanisms capable of generating paths that closely approximate the desired input path. This study also builds a publicly available database of over 5 million paths and their corresponding RSCR mechanisms. The database provides a solid foundation for training deep learning models. An application in the design of human upper-limb rehabilitation mechanism is presented. Several RSCR mechanisms closely matching the wrist and elbow path collected from human movements are found using our deep learning models. This application underscores the potential of RSCR mechanisms and the effectiveness of our model in addressing complex, real-world spatial mechanism design problems.

Determining appropriate process parameters in large-scale laser powder bed fusion (LPBF) additive manufacturing pose formidable challenges that necessitate advanced approaches to minimize trial-and-error during experimentation. This work proposed a data-driven approach based on stacking ensemble learning to predict the mechanical properties of Ti6Al4V alloy fabricated by large-scale LPBF for the first time. This method can adapt to the complexity of large-scale LPBF data distribution and exhibits a more generalized predictive capability compared to base models. Specifically, the stacking model utilized artificial neural network (ANN), gradient boosting regressor, kernel ridge regression, and elastic net as base models, with the Lasso model serving as the meta-model. Bayesian optimization and cross-validation were utilized for model optimization and training based on a limited data set, resulting in higher predictive accuracy compared to traditional artificial neural network model. The statistical analysis of the ANN and stacking models indicates that the stacking model exhibits superior performance on the test set, with a coefficient of determination value of 0.944, mean absolute percentage error of 2.51%, and root mean squared error of 27.64, surpassing that of the ANN model. All statistical metrics demonstrate superiority over those obtained from the ANN model. These results confirm that by integrating the base models, the stacking model exhibits superior predictive stability compared to individual base models alone, thereby providing a reliable assessment approach for predicting the mechanical properties of metal parts fabricated by the LPBF process.

The plastic gear is widely used in agricultural equipment, electronic products, aircraft, and other fields because of its light weight, corrosion resistance, and self-lubrication ability. However, it has a limited range of working conditions due to the low modulus and thermal deformation of the material, especially in high-speed and heavy-duty situations. A compensation modification method (CMM) is proposed in this paper to restrain the heat production of the plastic gear tooth surface by considering the meshing deformation, and the corresponding modification formulas are derived. Improving the position of the maximum contact pressure (CP) and the relative sliding velocity (RSV) of the tooth surface resulted in a 30% lower steady-state temperature rise of the modified plastic gear tooth surface than that of the unmodified plastic gear. Meanwhile, the temperature rise of plastic gear with CMM is reduced by 19% compared with the traditional modification of removal material. Then, the influences of modification index and the segment number of modification on the meshing characteristics of plastic gear with CMM are discussed, such as maximum CP and steady-state temperature rise, RSV, transmission error, meshing angle, and contact ratio. A smaller segment number and modification index are beneficial to reduce the temperature rise of plastic gear with CMM. Finally, an experiment is carried out to verify the theoretical analysis model.

Machining-induced damages encountered during the grinding of titanium alloys are a major setback for processing different components from these materials. Recent studies have shown that nanofluid (NF)-based minimum quantity lubrication (MQL) systems improved the machining lubrication and the titanium alloys’ machinability. In this work, the tribological characteristics of a palm oil-based tripartite hybrid NF (ZnO/Al₂O₃/Graphene Oxide, GO) are studied. The novel usage of the developed lubricants in MQL systems was examined during the grinding of Ti6-Al-4V (TC4) alloy. The NF was produced by mixing three weight percent mixtures (i.e., 0.1, 0.5, and 1 wt.%) of the nanoparticles in palm oil. A comprehensive tribological and physical investigation was conducted on different percentage compositions of the developed NF to determine the optimum mix ratio of the lubricant. The findings indicate that increasing the NF concentration caused an increment in the dynamic viscosity and frictional coefficient of the NFs. The tripartite hybrid NF exhibited superior tribological and physicochemical properties compared with the pure palm and monotype-based NFs. Moreover, the dynamic viscosity of the tripartite-hybrid-based NFs increased by 12%, 5%, and 11.5% for the Al₂O₃, GO, and ZnO hybrid NFs, respectively. In addition, the machining results indicate that the tripartite hybrid NF lowered the surface roughness, specific grinding, grinding force ratio, tangential, and normal grinding forces by 42%, 40%, 16.5%, 41.5%, and 30%, respectively. Hence, the tripartite hybrid NFs remarkably enhanced the tribology and machining performance of the eco-friendly lubricant.

In finite element analysis (FEA), optimizing the storage requirements of the global stiffness matrix and enhancing the computational efficiency of solving finite element equations are pivotal objectives. To address these goals, we present a novel method for compressing the storage of the global stiffness matrix, aimed at minimizing memory consumption and enhancing FEA efficiency. This method leverages the block symmetry of the global stiffness matrix, hence named the blocked symmetric compressed sparse column (BSCSC) method. We also detail the implementation scheme of the BSCSC method and the corresponding finite element equation solution method. This approach optimizes only the global stiffness matrix index, thereby reducing memory requirements without compromising FEA computational accuracy. We then demonstrate the efficiency and memory savings of the BSCSC method in FEA using 2D and 3D cantilever beams as examples. In addition, we employ the BSCSC method to an engine connecting rod model to showcase its superiority in solving complex engineering models. Furthermore, we extend the BSCSC method to isogeometric analysis and validate its scalability through two examples, achieving up to 66.13% memory reduction and up to 72.06% decrease in total computation time compared to the traditional compressed sparse column method.

Alumina dispersion-strengthened copper (ADSC), as a representative particle-reinforced metal matrix composite (PRMMC), exhibits superior wear resistance and high strength. However, challenges arise in their processability because of the non-uniform material properties of biphasic materials. In particular, limited research has been conducted on the reinforcement mechanism and behavior of particles during material cutting deformation of PRMMC with nanoscale particles. In this study, a cutting simulation model for ADSC was established, separating the nanoscale reinforcement particles from the matrix. This model was utilized to analyze the interactions among particles, matrix, and tool during the cutting process, providing insights into chip formation and fracture. Particles with high strength and hardness are more prone to storing stress concentrations, anchoring themselves at grain boundaries to resist grain fibration, thereby influencing the stress distribution in the cutting deformation zone. Stress concentration around the particles leads to the formation of discontinuous chips, indicating that ADSC with high-volume fractions of particle (VFP) exhibits low cutting continuity, which is consistent with the results of cutting experiments. The tool tip that is in contact with particles experiences stress concentration, thereby accelerating tool wear. Cutting ADSC with 1.1% VFP results in tool blunting, which increases the radius of cutting edge from 0.5 to 1.9 μm, accompanied with remarkable coating delamination and wear. Simulation results indicate that the minimum uncut chip thickness increases from 0.04 to 0.07 μm as VFP increases from 0.3% to 1.1%. In conjunction with scratch experiments, MUCT increases with the augmentation of VFP. Computational analysis of the specific cutting force indicates that particles contribute to the material’s size effect. The results of this study provide theoretical guidance for practical engineering machining of ADSC, indicating its great importance for the process design of components made from ADSC.

This study addresses the challenges of tendon-driven continuum robots in terms of high-performance joint design, high-accuracy and -efficiency mechanical modeling, and inverse kinetostatic-based control. First, a general design framework for rigid–flexible coupled continuum robots is proposed inspired by the Freedom and Constraint Topology theory. Based on this framework, a novel claw-type continuum robot with high torsion resistance, high-precision positioning, and excellent anti-buckling performance is developed. Consequently, a novel kinetostatic model named the separated beam equilibrium model (SBEM) is proposed by solving the equilibrium equations for each unit individually rather than recursively, which achieves high modeling accuracy and efficiency. Finally, an iterative inverse kinetostatic-based control method involving mechanic factors is proposed. Comparative experimental results demonstrate that the claw-type continuum robot outperforms the twin-pivot continuum robot in terms of torsion resistance by more than 300 times. Moreover, the SBEM achieves high morphology estimation accuracy with errors less than 2.91% of manipulator length and high efficiency with more than 20 times improvement for computation reduction compared with the conventional chained beam constraint model. Furthermore, the iterative inverse kinetostatic model-based control obtains a tip error less than 3.70% of manipulator length by only using the open-loop method. The proposed design, modeling, and control method exhibits vast potential for continuum robots when tackling challenging tasks such as inspection, maintenance, and medical surgery in confined and unstructured environments including engine flow paths, nuclear conduits, and human body cavities.

The selective laser melting (SLM) technique applied to high-entropy alloys (HEAs) has attracted considerable attention in recent years. However, its practical application has been restricted by poor surface quality. In this study, the capability of laser polishing on the rough surface of a Co-free HEA fabricated using SLM was examined. Results show that the initial SLM-manufactured (as-SLMed) surface of the Co-free HEA, with a roughness exceeding 3.0 μm, could be refined to less than 0.5 μm by laser polishing. Moreover, the microstructure, microhardness, and wear resistance of the laser-polished (LP-ed) zone were investigated. Results indicate that compared with the microhardness and wear resistance of the as-SLMed layer, those of the LP-ed layer decreased by 4% and 11%, respectively, because of the increase in grain size and reduction of the BCC phase. This study shows that laser polishing has an excellent application prospect in surface improvement of HEAs manufactured by SLM.