Search Results (327)

Structured CBN (cubic boron nitride) grinding wheels usually have a specially designed texture on their surface to reduce the grinding heat and grinding force. However, most structured grinding wheels are fabricated by electroplating, brazing, sintering, and mechanical or laser removal on the surface of conventional grinding wheels, which may have problems such as complicated processes, low processing efficiency, and unstable effects. In this paper, additive manufacturing was used to fabricate a radial straight groove-structured grinding wheel. Meanwhile, a corresponding mathematical model of the grinding wheel was also established considering the shape and position of the abrasive grains. Subsequently, the ground surface morphologies of the fabricated wheel and simulated wheel under different machining parameter conditions were compared to further prove the rationality of the simulated grinding wheel. The results showed that the ground surfaces of the fabricated wheel and simulated wheel had similar morphological characteristics. The trend in the surface roughness under the different machining parameter conditions was also analyzed and showed the same variation for fabricated and simulated wheels; the error rate was confined within 8%. This paper elucidates the grinding mechanism and surface morphology formation process of a radial straight groove-structured grinding wheel fabricated by additive manufacturing. Full article

Due to the random factors that influence grinding stability, hardness distribution appears in inhomogeneity at different locations on the hardened layer in grind-hardening technology. It may affect the wear resistance of parts. Therefore, in order to explore the influence mechanism of hardness homogeneity on the wear resistance comprehensively, grind-hardening and friction experiments on AISI 1045 steel are carried out. Then, the causes of inhomogeneous hardness distribution are analyzed, and the influence of hardness homogeneity on wear resistance is also discussed. Combining the Archard wear model, the wear process of the hardened layer is simulated for analyzing the effect of contact stress distribution and action range on material loss in the worn area and finally realizing the prediction of the wear depth. The results show that the difference in microstructure distribution caused by the nonlinear variation in grinding force is the fundamental reason for the hardness inhomogeneity of the hardened layer. The hardness homogeneity results in the wear resistance of the cut-out end being superior to that of cut-in end. Additionally, the error between the predictive and the experimental value of the wear depth with different parameters is between 3.6% and 11.3%, thereby verifying the effectiveness of the theoretical research. Full article

As one of the most important finishing machining means of internal helical gear, the residual stress that appears during profile grinding plays an important role in transmission performance and the service internal helical gear. In this research, the residual stress simulation model of internal helical gear profile grinding is established to optimize and predict grinding parameters by means of a neural network. The grinding process parameters (including grinding depth, grinding feed speed, and grinding wheel linear speed) are taken as variable factors. Through experimental verification, the maximum error of the simulation value is 12.8%. The radial basis function (RBF) neural network is introduced, and simulation data samples are used to train and test the residual stress prediction model. Three groups of unknown grinding parameters are predicted, and the relative errors between the predicted and measured values are 5.16%, 1.63%, and 3.39%, respectively. The results demonstrate that the RBF neural network residual stress prediction model proposed in this paper is accurate and feasible. At the same time, the residual stress prediction method provides a theoretical basis for optimizing and controlling the precision of internal helical gear profile grinding. Full article

In this paper, we proposed a novel method for grinding trajectory planning on large-curved forgings to improve grinding performance and grinding efficiency. Our method consists of four main steps. Firstly, we conducted simulations and analyses on the contact state and contact pressure between the grinding tool and curved workpieces, and explored different grinding methods. Based on the Preston equation, a material removal model was established to analyze the grinding force. Secondly, we proposed an adaptive impedance control method based on grinding force analysis, which can control the contact force indirectly by adjusting the end position of the robot. To address the inability of impedance control to adjust impedance parameters in real time, a control strategy involving online estimation of environmental position and stiffness is adopted. Based on the Lyapunov asymptotic stability principle, an adaptive impedance control model is established, and the effectiveness of the adaptive algorithm is verified through simulation. Thirdly, Position correction is realized through gravity compensation of the grinding force and discretization of the impedance control model. Subsequently, a dynamic trajectory adjustment strategy is proposed, which integrates position correction for the current grinding point and position compensation for the next grinding point, to achieve the force control objective in the grinding process. Finally, a constant force grinding experiment was conducted on large-curvature blades using a robotic automatic grinding system. The grinding system effectively removed the knife marks on the blade surface, resulting in a surface roughness of 0.5146 μm and a grinding efficiency of approximately 0.89 cm²/s. The simulation and experimental results indicate that the smoothness and grinding efficiency of the blades are superior to the enterprise’s existing grinding technology, verifying the feasibility and effectiveness of our proposed method. Full article

To date, the grinding behavior of saprolite and lateritic overburden mixtures remains poorly understood. The Bond Work Index (BWI) is the principal indicator used to determine the specific energy consumption during the grinding process. To establish the F₈₀ and P₈₀ values, granulometric distribution models—Rosin–Rammler (RR), Gates–Gaudin–Schuhmann (GGS), and the Swebrec function (SWEF)—were evaluated. The mineral phases of the feed samples were analyzed by X-ray powder diffraction. This study provides evidence that the RR function is the most suitable for simulating the particle size distribution of the feed material, with residual errors below 6.30% and a coefficient of determination (R²) exceeding 97%. After the grinding equilibrium cycle is reached, the SWEF model proves to be the most appropriate, exhibiting residual errors under 3.50% and R² values above 98%. BWI reveals that saprolite is the most difficult ore to grind, with specific energy consumption increasing from 16.38 kWh/t to 25.50 kWh/t as the proportion of saprolite in the mixture rises. This reflects a clear upward trend, as confirmed by a fitted model with an R² of 98.54%. In contrast, the grindability index (Gbp) decreases, indicating that the material becomes increasingly resistant to grinding as the saprolite content increases. This may be attributed to inherent material properties, such as hardness, or to physical phenomena related to fragmentation. The declining Gbp further suggests that greater energy input is required to achieve additional particle size reduction. Overall, the findings demonstrate that saprolite is inherently difficult to grind and behaves according to its own grinding characteristics, regardless of whether it is processed alone or in combination with lateritic overburden. Full article

To investigate the influence of surface films on the material removal mechanism of single-crystal silicon during nanogrinding, molecular dynamics (MD) simulations were performed under different surface-film conditions. The simulations examined atomic displacements, grinding forces, radial distribution functions (RDF), phase transformations, temperature distributions, and residual stress distributions to elucidate the damage mechanisms at the surface and subsurface on the nanoscale. In this study, boron nitride (BN) and graphene films were applied to the surface of single-crystal silicon workpieces for nanogrinding simulations. The results reveal that both BN and graphene films effectively suppress chip formation, thereby improving the surface quality of the workpiece, with graphene showing a stronger inhibitory effect on atomic displacements. Both films reduce tangential forces and mitigate grinding force fluctuations, while increasing normal forces; the increase in normal force is smaller with BN. Although both films enlarge the subsurface damage layer (SDL) thickness and exhibit limited suppression of crystalline phase transformations, they help to alleviate surface stress release. In addition, the films reduce the surface and subsurface temperatures, with graphene yielding a lower temperature. Residual stresses beneath the abrasive grain are also reduced when either film is applied. Overall, BN and graphene films can enhance the machined surface quality, but further optimization is required to minimize subsurface damage (SSD), providing useful insights for the optimization of single-crystal silicon nanogrinding processes. Full article

The roller press crushing of ore is a complex process involving the interplay of multiple factors. Roller dimensions, gap settings, and rotational speed all influence this process, which in turn affects the comprehensive crushing performance of the high-pressure grinding rolls (HPGR). Therefore, to simultaneously enhance the HPGR’s size reduction effectiveness (SRE) and throughput while controlling its energy consumption, wear, and edge effect, multi-objective parameter optimization of the HPGR is required. This study utilizes the Discrete Element Method (DEM) to simulate ore comminution within an HPGR. By first dividing the release zone into segments, the particle size distribution of the crushed product at different locations within this zone is investigated. Then, the influence of various factors on the SRE at different locations within HPGR is examined through single-factor experiments. Subsequently, the relative influence of roller diameter, roller width, roller speed, and roll gap on the comprehensive crushing performance of the HPGR is determined through signal-to-noise ratio (SNR) analysis and analysis of variance (ANOVA). Finally, multi-objective parameter optimization of the roller press crushing is conducted based on grey relational analysis (GRA), incorporating the weights assigned to different response target. The results indicate that the proportion of unbroken ore particles is relatively significant, primarily due to the edge effect. Further analysis reveals that along the horizontal diameter of the rollers, regions closer to the roller surface exhibit better SRE. Additionally, roller speed is identified as the most influential factor affecting the uniformity of SRE in the HPGR. The application of GRA to the multi-objective optimization of roller press crushing enables effective balancing of the comprehensive crushing performance in HPGR. Full article

The cup-shaped grinding wheels with arc-shaped edges provide a satisfactory precision grinding solution for high-accuracy optical surfaces on hard and brittle materials. However, the complex profile of the arc-shaped edges of cup-shaped grinding wheels makes them challenging to truing. This paper proposes an on-machine truing technique targeting cup-shaped grinding wheels with arc-shaped cutting edge. First, a mathematical model was established to simulate the three-axis of on-machine truing the arc-shaped cutting edge using a diamond roller. Based on this model, a theoretical analysis is conducted to investigate the impact of tool setting errors, measurement errors of the diamond roller, and the pose error on truing accuracy. A compensation method was proposed, and experimental results validated its effectiveness. To investigate the grinding performance of cup-shaped grinding wheels after truing, a complex component is ground using a truing diamond grinding wheel. The experimental results demonstrate that this method enables precise on-machine truing of the arc-shaped edges of cup-shaped grinding wheels and is efficient. The average dimensional accuracy of the grinding wheel’s arc-shaped edge is reduced to 1.5 μm, with the profile accuracy (PV) of 0.89 μm. Full article

Reaction-bonded silicon carbide (RB-SiC) is a high-performance ceramic material known for its excellent mechanical, thermal, and chemical properties. It contains phases with different mechanical properties, which introduce complex machining mechanisms. In the present work, molecular dynamics (MD) simulation was conducted to investigate the effect of abrasive size on the nano-grinding mechanism of RB-SiC. The surface morphology and subsurface deformation mechanism were investigated. The simulation results suggest that when a small abrasive is used, the surface swelling of SiC is primarily generated by the bending and tearing of SiC at the interfaces. As the abrasive radius increases, the surface swelling is mainly formed by Si atoms, which is identified as elastic recovery. Meanwhile, the material removal rate gradually decreases, and the depth of plastic deformation is obviously increased. Stocking of Si is more apparent at the interface, and obvious sliding of SiC grains is observed, forming edge cracks at the margin of the workpiece. In the subsurface workpiece, the high-pressure phase transition (HPPT) of Si is promoted, and the squeeze of disordered Si is obvious with more dislocations formed when larger abrasive is used. Full article

Ultrasonic-assisted machining (UAM) has emerged as a transformative technology for increasing material removal efficiency, improving surface quality and extending tool life in precision manufacturing. This review specifically focuses on the application of it to titanium aluminide (TiAl) alloys. These alloys are widely used in aerospace and automotive sectors due to their low density, high strength and poor machinability. This review covers various aspects of UAM, including ultrasonic vibration-assisted turning (UVAT), milling (UVAM) and grinding (UVAG), with emphasis on their influence on the machinability, tool wear behavior and surface integrity. It also highlights the limitations of single-energy field UAM, such as inconsistent energy transmission and tool fatigue, leading to the increasing demand for multi-field techniques. Therefore, the advanced machining strategies, i.e., ultrasonic plasma oxidation-assisted grinding (UPOAG), protective coating-assisted cutting, and dual-field ultrasonic integration (e.g., ultrasonic-magnetic or ultrasonic-laser machining), were discussed in terms of their potential to further improve TiAl alloys processing. In addition, the importance of predictive force models in optimizing UAM processes was also highlighted, emphasizing the role of analytical and AI-driven simulations for better process control. Overall, this review underscores the ongoing evolution of UAM as a cornerstone of high-efficiency and precision manufacturing, while providing a comprehensive outlook on its current applications and future potential in machining TiAl alloys. Full article

A mill relining manipulator is key maintenance equipment for liners exchanged and operated by workers inside a grinding mill. To improve the operation efficiency and safety, real-time path planning and end deformation compensation should be performed prior to actual execution. This paper proposes a five-dimensional digital twin framework to realize virtual–real interaction between a physical manipulator and virtual model. First, a real-time digital twin scene is established based on OpenGL. The involved technologies include scene rendering, a camera system, the light design, model importation, joint control, and data transmission. Next, different solving methods are introduced into the service space for relining tasks, including a kinematics model, collision detection, path planning, and end deformation compensation. Finally, a user application is developed to realize real-time condition monitoring and simulation analysis visualization. Through comparison experiments, the superiority of the proposed path planning algorithm is demonstrated. In the case of a long-distance relining task, the planning time and path length of the proposed algorithm are 1.7 s and 15,299 mm, respectively. For motion smoothness, the joint change curve exhibits no abrupt variation. In addition, the experimental results between original and modified end trajectories further verified the effectiveness and feasibility of the proposed end effector compensation method. This study can also be extended to other heavy-duty manipulators to realize intelligent automation. Full article

This paper presents a control method for achieving precise robotic contact on complex and curved surfaces in manufacturing and automation. The method combines smooth trajectory planning with contact force control to improve finishing accuracy while reducing processing time. It integrates a Bézier curve with a simplified hexic polynomial implemented through a position-based impedance controller that is enhanced by a novel force corrector unit. The model is referred to as the Adaptive Bézier–Based Impedance Constant Force Controller (ABBIFC), where the Bézier curve length is calculated using Simpson’s rule, and surface orientations are interpolated using quadratic quaternions. A hexic polynomial velocity profile ensures consistent motion speed throughout the process. This method effectively regulates both contact force and positional accuracy, resulting in high-quality surface finishes. Simulation studies and real-time polishing experiments demonstrate the system’s capability to accurately track path, speed, and force, with significantly reduced force errors. This approach advances robotic automation in applications such as polishing, grinding, and other surface finishing tasks by ensuring smooth motion and precise force control. Full article

As global manufacturing faces rising energy costs, environmental pressures, and machining precision, the development trends of the machine tools are moving towards lightweight and high-rigidity structures. While those approaches of increasing key component geometrical size or enhancing rib design do enhance rigidity performance, they also usually increase weight, which conflicts with the goals of achieving high performance and environmental sustainability. Therefore, how to achieve system lightweightness while maintaining or enhancing structural rigidity has become a key research challenge. This study adopts a biomimetic design approach, drawing inspiration from the natural growth features of biological structures. By integrating these natural structural features, the design aims to enhance rigidity while reducing weight. Static and modal analyses are conducted firstly by using FEM software to simulate the total deformation, natural frequency, and modal shape, respectively. The biomimetic designs are then performed on those subsystems in a grinding machine-tool, which exhibit larger deformation and weaker stiffness by incorporating the structural features of leaf veins, cacti, and bamboos. Single or multiple structural feature combinations are constituted during the biomimetic design processes for worktable, base, and column subsystems, and the natural frequencies and weight obtained from the numerical analysis were compared subsequently to identify the better bionic subsystems that replace the corresponding ones originally assembled in the grinding machine-tool finally. The results show that one of the first three mode natural frequencies of a better bionic worktable (leaf vein and cactus) is increased up to 7.07%, with a 1.12% weight reduction. A better bionic base (leaf vein) with corner trimming exhibits a 14.04% increase in natural frequency and a 2.04% weight reduction. Similarly, a better bionic column (bamboo) achieves a 5.58% increase in natural frequency and a 0.14% weight reduction. After these better bionic subsystems are substituted in the grinding machine-tool, one of the first three mode natural frequencies is increased up to 14.56%, the weight is reduced by 1.25%, and the maximum total deformation is decreased by 39.64%. The maximum total deformation for the headstock is reduced by 26.95% after the original grinding machine-tool is replaced by better bionic subsystems. The increases in the specific stiffness for these better bionic subsystems are also investigated in this study to illustrate the effectiveness of the biomimetic designs. Full article

Monocrystalline silicon is an excellent semiconductor material for integrated circuits. Its surface quality has an enormous effect on its service life. The surfaces are formed by ultra-precision machining using nano-grinding, one of the technologies that can achieve surface roughness at the nano- or sub-nano-scale. Therefore, subsurface damage of monocrystalline silicon in nano-grinding was studied by establishing a molecular dynamics simulation model, and the impact of machining parameters on the force–thermal behavior was analyzed. The results reveal that the mechanism of subsurface damage is mainly structural phase transformation and amorphization. In nano-grinding of monocrystalline silicon, the tangential grinding force has a relatively major role in material removal. With increasing grinding depth and grinding speed, the grinding heat rises, and a certain degree of high temperature strengthens the toughness of the material, improving the subsurface quality of monocrystalline silicon. Therefore, subsurface damage in monocrystalline silicon can be controlled by reducing the grinding depth and increasing the grinding speed. Full article

Volumetric error decoupling is a critical prerequisite for effective error compensation. In this paper, the forward volumetric error model is established using the screw theory. Additionally, the Jacobian matrix based on the product of exponential is derived to construct the linear relationship between the volumetric error and the axis motion and decouple the volumetric error model. To address the limitation of compensation motion, a step-by-step decoupling method is proposed, where attitude and position errors are compensated sequentially. After detecting the actual geometric errors of the grinding machine, the volumetric error can be determined, and the compensation motion commands for each axis are calculated to correct the volumetric error. The simulation result shows that the mean value of the comprehensive error ranges can be reduced from 19.7 μm to 1.8 μm, demonstrating the effectiveness of the proposed method. Full article