Search Results (86)

Precision dicing with diamond wheels is a key technology in semiconductor dicing, integrated circuit manufacturing, aerospace, and other fields, owing to its high precision, high efficiency, and broad material applicability. As a critical processing stage, a comprehensive analysis of dicing technologies is essential for improving the machining quality of hard-and-brittle optoelectronic materials. This paper reviews the core principles of precision diamond wheel dicing, including dicing processes and blade preparation methods. Specifically, it examines the dicing mechanisms of composite and multi-mode dicing processes, demonstrating their efficacy in reducing defects inherent to single-mode approaches. The review also examines diverse preparation methods for dicing blades, such as metal binder sintering and roll forming. Furthermore, the roles of machine vision and servo control systems are detailed, illustrating how advanced algorithms facilitate precise feature recognition and scribe line control. A systematic analysis of key components in grinding wheel dicer is also conducted to reduce dicing deviation. Additionally, the review introduces models for tool wear detection and discusses material removal mechanisms. The influence of critical process parameters—such as spindle speed, feed rate, and dicing depth—on dicing quality and kerf width is also analyzed. Finally, the paper outlines future prospects and provides recommendations for advancing key technologies in precision dicing, offering a valuable reference for subsequent research. Full article

The honeycomb sealing surface serves as the critical sealing structure between the rotor and stator of an engine, and its sealing performance significantly impacts engine efficiency. To address the challenge of effectively controlling the overcutting depth during the electrolytic grinding of honeycomb sealing surfaces, this study quantitatively determined the actual volumetric equivalent electric charge of the honeycomb grid surface based on Faraday’s law of electrolysis. Nonlinear fitting was employed to establish the decay characteristics of current density and machining efficiency. Machining experiments were designed with voltage and feed speed set as independent variables, and an empirical model coupling the electrolytic proportion with overcutting depth was fitted on the basis of the obtained experimental results. The new parameters were validated, with the model’s predicted values showing an error of approximately 3.5% compared to actual measurements. By selecting the processing parameters using the established empirical prediction model, the overcutting depth of honeycomb seals can be controlled within 0.01 mm while ensuring excellent surface quality, which further meets the high-precision machining requirements for key components such as aviation engine seals. Full article

To address the tool calibration challenge in robotic systems equipped with grinding tools, this paper proposes a high-precision method utilizing a stylus assembly and the Particle Swarm Optimization (PSO) algorithm. A global optimization strategy is implemented, which simultaneously identifies and compensates for coupled error sources, including the robot’s kinematic (DH) parameters, the tool coordinate frame (TCF), and the stylus tip’s spatial position. This approach transforms the complex calibration task into a constrained, high-dimensional optimization problem. The experimental results demonstrate the method’s effectiveness, reducing the final calibration Root Mean Square Error (RMSE) to below 0.1 mm. Validation through a practical grinding experiment confirmed a significant improvement in machining accuracy, with the workpiece’s axis deviation from the ideal model decreasing from 1.477° to 0.326°, and the maximum contour error being reduced from 1.4 mm to under 0.3 mm. This study provides a robust, low-cost technical solution for tool calibration in complex industrial applications. 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

In order to achieve the high-performance machining of silicon carbide (SiC) ceramics, longitudinal–torsional ultrasonic vibration (LTUV) was introduced into precision machining, and a systematic investigation into the effects of various process parameters on the critical cutting depth and surface quality was conducted. This investigation was undertaken with a view to exploring the ultrasonic vibration-assisted grinding mechanism of SiC ceramics. Firstly, the kinematic model of single abrasive grain trajectory and the maximum unaltered cutting thickness during longitudinal–torsional ultrasonic vibration-assisted grinding (LTUVG) was established to explore its unique grinding characteristics. On this basis, the theoretical modeling of critical cutting depth in SiC ceramics under LTUVG conditions was developed. This was then verified through longitudinal–torsional ultrasonic scratching (LTUS) experiments, and the theoretical analysis and test results prove that compared with normal scratching, the quality of SiC grooves are significantly improved by means of LTUS. During LTUS experiments, the dynamic fracture toughness, strain rate of SiC, and high-frequency ultrasonic excitation significantly enhances SiC performance, increasing the critical cutting depth and expanding the plastic removal region, so it is easy for LTUVG to yield the better surface quality in machined SiC ceramics, which provides important scholarly support for achieving the low-damage machining of SiC ceramics. 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

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

Buckwheat is a highly nutritious coarse grain crop, yet its industrial processing has long faced two major challenges: the low whole-kernel rate of domestic dehullers and the poor local adaptability of imported equipment. To address these problems, a novel grinding disc-type dehulling machine was developed, featuring upper and lower discs with alternating deep–shallow composite textures to reduce kernel breakage and improve whole kernel rate. A 0–10 mm adjustable gap mechanism was incorporated to suit different buckwheat varieties and particle sizes, enhancing dehulling efficiency. Buckwheat grains were classified into four size ranges: 4.0–4.5 mm, 4.5–5.0 mm, 5.0–5.3 mm, and 5.3–5.7 mm. For all sizes, the optimal rotational speed was 12 r/min, with corresponding optimal gaps of 2.53 mm, 2.80 mm, 3.20 mm, and 3.40 mm, respectively. The whole-kernel rates under these conditions were 32.9%, 37.5%, 45.6%, and 55.1%, respectively, all above 30%, showing substantial improvement. For the 4.5–5.0 mm fraction, orthogonal tests revealed that a small gap (2.859 mm) achieved a dehulling rate of 89.9% and a whole-kernel rate of 38.03%, making it suitable for mass production. A larger gap (3.288 mm) combined with secondary dehulling increased the cumulative whole kernel rate to 50.26%, which is advantageous for producing high value-added products. The novel grinding disc structure balanced frictional and compressive forces on kernels, while the adjustable gap design improved adaptability. Combined with size classification and parameter optimization, this approach provides precise processing schemes for various buckwheat varieties and offers both theoretical and practical value for industrial application. Full article

Given that grinding robots are easily affected by internal and external disturbances when machining complex surfaces with high precision, in this study, an adaptive robust impedance control method combining a radial basis function neural network (RBFNN) and sliding mode control (SMC) is proposed. In a Cartesian coordinate system, we first use the universal approximation ability of the RBFNN to accurately identify and actively compensate for complex unknown disturbances in robot dynamics online. Then, an improved sliding mode impedance controller, which uses robust sliding mode control to effectively suppress the influence of RBFNN identification error and residual disturbance on trajectory tracking and ensure the accuracy of impedance control, is implemented. This approach improves the control performance and overcomes the inherent chattering phenomenon of the traditional sliding mode. Full article

Properly refrigerating hard-to-cut alloys during grinding is key to achieve high quality, strict tolerances, and good surface finishing. Nonetheless, literature about the influence of cooling-lubrication conditions (CLCs) on dimensional accuracy of ground components is still scarce. Thus, this work aims to evaluate surface quality, grinding power, and dimensional accuracy of Inconel 718 workpieces after grinding with silicon carbide grinding wheel at different grinding conditions. Four different CLCs were tested: flood, minimum quantity of lubrication (MQL) without graphene, and with multilayer graphene (MG) at two distinct concentrations: 0.05 and 0.10 wt.%. Different radial depths of cut values were also tested. The results showed that the material’s removed height increased with radial depth of cut, leading to coarse tolerance (IT) grades. Machining with the MQL WG resulted in higher dimensional precision with an IT grade varying between IT6 and IT7, followed by MQL MG 0.10% (IT7), MQL MG 0.05% (IT7-IT8), and flood (IT8). The lower tolerances achieved with MG were attributed to the lowering in the friction coefficient of the workpiece material sliding through the abrasive grits with no material removal (micro-plowing mechanism), thereby reducing grinding power and the removed height in comparison to the other CLC tested. Full article

The thermal error of the high-power grinding motorized spindle, caused by heating, seriously affects machining accuracy. In this paper, an ensemble learning algorithm is used to predict the thermal error of a high-precision motorized spindle. The subsequent problem of thermal error compensation can be effectively solved by a suitable thermal error model, which is crucial for improving the machining accuracy of the actual machining process. Firstly, the steady-state temperature field of the grinding motorized spindle is analyzed and used to determine the position of the sensors. Then, a signal acquisition instrument is used to monitor real-time temperature data. After that, experimental results are obtained, followed by verification. Finally, based on experimental data and the optimization results of temperature measurement points, temperature data are used as the input variable, and thermal deformation data are used as the output variable. The ensemble learning model is composed of different weak learners, which include multiple linear regression, back-propagation, and radial basis function neural networks. Different weak learners are trained using datasets separately, and the output of the weak learners is used as input to the model. Through integrating strategies, an ensemble learning model is established and compared with a weak learner. The error residual set of the ensemble learning model remains within [−0.2, 0.2], and the prediction performance shows that the ensemble learning model has a better predictive effect and strong robustness. Full article

Addressing the issues of easy deformation and difficult-to-control machining accuracy of thin-walled bearing rings during precision grinding due to clamping forces, existing research mainly employs methods such as elastic clamping, hydraulic control, pneumatic control, and vacuum adsorption to tackle the clamping problem. However, these methods still suffer from problems such as uneven clamping force, insufficient adaptability, and limited machining accuracy. In this paper, a novel fixture suitable for precision grinding of thin-walled bearing rings is designed. By analyzing the working principle of the fixture and considering the processing characteristics of thin-walled bearing rings, the fixture structure is designed and optimized to enhance its clamping stability and machining accuracy. Modal analysis and stress-displacement analysis are conducted to verify the stability and performance of the new fixture during the machining process. The research results show that the fixture can effectively reduce the deformation of thin-walled bearing rings, improve machining quality and efficiency, and provide a feasible solution for high-precision grinding of thin-walled bearing rings. Full article

This study developed a movable, high-precision laser measurement system for assessing wear on cup grinding wheels. The proposed setup employs a Keyence CL-P070 laser sensor with a resolution of 0.25 μm and has a simple installation process that supports flexible deployment on multiple workstations. Unlike traditional static configurations, the compact design requires minimal adjustment and enables versatile positioning across operational environments. An automated measurement procedure was developed that can capture changes in the grinding wheel profile between grinding cycles. The experimental results indicate that the proposed system has high repeatability and accuracy in detecting the subtle progression of wear in cup grinding wheels. The proposed approach provides a user-friendly on-machine measurement solution that can improve quality control and operational efficiency in industrial grinding processes. Full article

In the process of herringbone gear grinding, excessive grinding force leads to a large increase in grinding specific energy. A large increase in the specific grinding energy can easily lead to an increase in the transient cutting load. It leads to grinding burn, tooth surface crack and other undesirable phenomena, which ultimately affect the surface quality and service performance of the workpiece. This paper is based on the contact mechanics of workpiece materials. The number of dynamic effective abrasive particles is considered. Combined with the mechanism of grinding force, the model is developed. Based on the consideration of the wear characteristics of the grinding wheel and the structure parameters of the gear itself, the grinding force model was modified. The accuracy of grinding force model is improved by dividing the effective contact angle of grinding grains into four cases. The experimental results show that the normal grinding force error reaches 10.73% and the tangential grinding force error reaches 10.34%. The model reveals the grinding mechanism, optimizes grinding parameters and improves grinding efficiency. It provides a new way for high-precision machining of aerospace precision herringbone gear. Full article