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.
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.
Water hydraulic technology is a potential application to deep-sea manipulators and their proportional valves. In the ocean, water is a better choice as the working medium than mineral oil because of its environmentally friendly advantages. However, no water hydraulic proportional valve for deep sea exists yet. In this study, a novel water hydraulic rotary proportional valve with a four-way, three-position principle and a plane sealing method for the environment-friendly manipulator is invented. The static and dynamic performance of the proportional valve is studied using a mathematical model and experiments. A valve-control swing cylinder system, which simulates the working state of the manipulator, is also facilitated in a deep-sea simulation device for simulating a depth of 6500 m in the ocean. Results show that the numerical and experimental data match well. The proportional valve can achieve zero leakage, and the dead zone is approximately 10%. The bandwidths are 30 and 6 Hz when the input signal amplitude is 5% and 100% of the valve’s full stroke, respectively. The proportional valve can accurately control the swing cylinder on the manipulator’s elbow joint with a rotation angle error of ±0.1°. The rotary proportional valve has excellent application to deep-sea manipulators.
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 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.
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.
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.
