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This paper presents an experimental investigation of the displacement characteristics of a hydrostatic spindle used in a medium-sized circular grinding machine. The study focuses on the influence of lubricant pressure and oil viscosity on spindle displacement under varying load conditions. Experiments were conducted using a specially designed testing bench, which allowed precise control of pump pressure and oil viscosity. The results demonstrate a significant impact of both parameters on spindle displacement. An increase in oil viscosity leads to a decrease in displacement, with the optimal viscosity identified as 2 mPa.s. Similarly, higher pump pressure results in reduced displacement, with 5 MPa being the optimal pressure for the system studied. These findings highlight the importance of carefully selecting lubricant properties and operating conditions to achieve optimal machining accuracy and stability. This research provides valuable insights into the understanding and optimization of hydrostatic bearing systems in precision machining applications. [ABSTRACT FROM AUTHOR]

The state of a grinding wheel directly affects the surface quality of the workpiece. The monitoring of grinding wheel wear state can allow one to efficiently identify grinding wheel wear information and to timely and effectively trim the grinding wheel. At present, on-line monitoring technology using specific sensor signals can detect abnormal grinding wheel wear in a timely manner. However, due to the non-linearity and complexity of the grinding wheel wear process, as well as the interference and noise of the sensor signal, the accuracy and reliability of on-line monitoring technology still need to be improved. In this paper, an intelligent monitoring system based on multi-sensor fusion is established, and this system can be used for precise grinding wheel wear monitoring. The proposed system focuses on titanium alloy, a typical difficult-to-process aerospace material, and addresses the issue of low on-line monitoring accuracy found in traditional single-sensor systems. Additionally, a multi-eigenvalue fusion algorithm based on an improved support vector machine (SVM) is proposed. In this study, the mean square value of the wavelet packet decomposition coefficient of the acoustic emission signal, the grinding force ratio of the force signal, and the effective value of the vibration signal were taken as inputs for the improved support vector machine, and the recognition strategy was adjusted using the entropy weight evaluation method. A high-precision grinding machine was used to carry out multiple sets of grinding wheel wear experiments. After being processed by the multi-sensor integrated precision grinding wheel wear intelligent monitoring system, the collected signals can accurately reflect the grinding wheel wear state, and the monitoring accuracy can reach more than 92%. [ABSTRACT FROM AUTHOR]

Copyright of Machine Tool & Hydraulics is the property of Guangzhou Mechanical Engineering Research Institute (GMERI) and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

To enhance the static and dynamic performance of the grinding wheel spindle system, with the gear grinding machine (YKF2060) as the research object, a static mechanics model of the spindle system was established based on Castigliano's theorem, taking into account the equivalent effect of the triple-point contact ball bearing at the front end of the spindle. Meanwhile, based on the overall transfer matrix method, a dynamic model of the main spindle–eccentric shaft dual-rotor system was established, taking into account the effects of shear deformation and gyroscopic moments. On this basis, the effect of the spindle span, the front and rear overhang of the eccentric shaft, and the bearing stiffness on the static stiffness and first-order critical speed of the system was analyzed. Finally, static stiffness experiments, modal tests, and finite element simulation models were conducted to verify the static and dynamic models. The results show that the stiffness of the front outer bearing has the greatest influence on the static and dynamic performance of the system, while the stiffness of the rear inner bearing has the least influence. The relative errors of the static stiffness and the first two natural frequencies between static stiffness experiments, modal tests, and finite element simulation models are less than 10%, and the mode shapes match well. The established static and dynamic model can effectively reflect both the static and dynamic characteristics of the spindle system. [ABSTRACT FROM AUTHOR]

Copyright of Machine Tool & Hydraulics is the property of Guangzhou Mechanical Engineering Research Institute (GMERI) and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The complexity of composite grinding movement, the variety of machining features and available grinding wheels, and the changing working conditions pose a challenge to the rapid programming of safe and efficient composite grinding procedure. The procedure optimisation plays an important role in solving this difficult problem. Therefore, a procedure optimisation method is proposed for composite grinding based on a Digital Twin (DT) system, which takes procedure time as optimisation objective to achieve high efficiency and ensures process rationality and safety by constructing corresponding constraint conditions. Moreover, the actual working conditions mapped into the DT system, such as workpiece parameters, machining requirements, grinding wheel parameters and status, machine tool motion position, and so on, are obtained to update the parameters involved in the optimisation model. And thus, the proposed method has the ability to timely find the optimal procedure under changing working conditions. In addition, a combination algorithm based on genetic algorithm (GA) and dynamic programming is proposed, which greatly reduces the search space of GA and realises the two-class co-optimisation of grinding wheel selection and procedure. Finally, the case study verifies the effectiveness of the proposed method to reduce procedure time and the dynamic response ability to changing working conditions. [ABSTRACT FROM AUTHOR]

This research is based on the modeling, internal structure analysis, and automation of a cassava grinding machine. For the design, the single-cylinder grinder was chosen due to its advantages, notably simplicity and versatility. The grinding method used in this type of grinder is compression. The mechanical design and simulation software first allowed for the design of the grinder and then for performing static analyses under three loads (P1 = 10000N, P2 = 15000N, and P3 = 20000N). The results of these tests led to the selection of P3 = 20000N as the straightening load, with a maximum stress value in the static analysis of 88,18 MPa and a maximum deformation of 0,000358 under the force of load P3. Additionally, the frequency analysis distinguished five resonance modes. The results for each mode concluded that no resonance would affect the machine, thus ensuring stable operation. Furthermore, fatigue, frequency, and safety factor studies showed that the machine could withstand a load of m3 = 2000 kg without damage, with a service life of 1e+09 cycles. Considering the total number of life cycles, which is 1e+09 cycles, this means that the machine will have a service life of 347000 years, with a total grinding output of 2e+12 kg of cassava and 5840000 kg per year. The factor of safety is FoS = 1,78, indicating a sufficient margin for safe operating conditions. The automation of the grinder was carried out using a GRAFCET model and a sophisticated human-machine interface (HMI), providing an additional safety point for the machine and allowing the operator to monitor the operation via a simple graphical interface. This automation enables continuous operation with minimal human intervention, thereby improving the efficiency and safety of the cassava grinding process. [ABSTRACT FROM AUTHOR]

This paper presents a method of calculating and correcting grinding face gears on an internal gear grinding machine. The generating principle of face gears is studied, and the feasibility of grinding motion on an internal gear grinding machine is analyzed. Then, the motions that need to be followed for grinding are analyzed based on the gear machine tool structure. Four main error sources causing tooth surface deviation in the grinding movements are proposed. The mathematical modeling of the grinding of face gears containing proposed error sources on an internal gear grinding machine is accurately established. The influence of the error sources on the topological deviations of the tooth surface is explored. A sensitivity matrix is established for the influence of various error factors on the tooth surface deviations. The correction values of each error factor are obtained in the case of existing tooth surface deviations. Finally, a virtual machining experiment is conducted, which proves the accuracy of the proposed method for characterizing grinding and realizing corrections. [ABSTRACT FROM AUTHOR]

BK7 glass, with its outstanding mechanical strength and optical performance, plays a crucial role in many cutting-edge technological fields and has become an indispensable and important material. These fields have extremely high requirements for the surface quality of BK7 glass, and any small defects or losses may affect its optical performance and stability. However, as a hard and brittle material, the processing of BK7 glass is extremely challenging, requiring precise control of machining parameters to avoid material fracture or excessive defects. Therefore, how to obtain the required surface quality with lower cost machining techniques has always been the focus of researchers. This article introduces the properties, application background, machining methods, material removal mechanism, and surface and subsurface damage of optical glass BK7 material. Finally, scientific predictions and prospects are made for future development trends and directions for improvement of BK7 glass machining. [ABSTRACT FROM AUTHOR]

One of the most crucial parameters in the field of machine tool engineering is the stiffness of the machine tool spindle, as it has a direct impact on the precision and accuracy of machining. This paper presents a research on the effects of geometric parameters of capillary tubes, including diameter (dc) and length (lc), on the stiffness of hydrostatic bearings in machine tools. The oil pump pressure in this study was set at a range from 3 MPa to 5 MPa. The simulation results demonstrate the relationship between the capillary geometric parameters, pressure, and the stiffness of the hydrostatic spindle unit. These findings indicate a direct relationship between stiffness and pump pressure, with increases in pressure also raising lubricant temperature, which may impair cooling. The shape of the capillary directly affects the ratio of oil chamber pressure to pump pressure, which in turn affects hardness. The results of the study indicate that for spindles in machine tools, a dc ranging from 0.3 mm to 0.6 mm and a (lc /dc) ratio of between 20 and 100 are feasible based on the given stiffness requirement. It is possible to select an appropriate set of capillary parameters to achieve the best stiffness with specific requirements under the working conditions of hydrostatic bearings based on the results of the simulation analysis. [ABSTRACT FROM AUTHOR]

An optimization design was carried out based on a back propagation (BP) neural network and a genetic algorithm (GA) to improve the stiffness and accuracy of the self-developed MGK6030 five-axis tool grinding machine. First, finite element analysis was carried out on the whole grinding machine based on ANSYS Workbench, and the key parts were found to be the grinding wheel headstock, B axle box body, and column. Sensitivity analysis was carried out after the model parameterization, and ten parameters, which affect the quality, maximum deformation, and first-order mode, were obtained. These parameters were used as input variables. A total of 235 sets of sample data were obtained by using the optimal overall performance of the grinder for the target (large first-order natural frequency, small deformation, and mass). The BP neural network was then used to fit the nonlinear coupling relationship between the input and the output. Thereafter, the optimization function of the GA was used to perform multi-objective optimization in the specified range. Finally, the parameters are verified by software simulation and prototype test. Results showed that the maximum deformation of the optimized machine tool is reduced by 21%, and the first four order natural frequencies are increased by 6.36%, 9%, 6.4%, and 2.84%. The positioning accuracies of the linear axis and rotary axis are increased by 22% and 21%, respectively, which demonstrates the effectiveness of the optimization scheme and provides theoretical and technical support for similar optimization problems. [ABSTRACT FROM AUTHOR]

Zirconia ceramic (ZrO2) has been widely utilized in industrial areas for its excellent physical and mechanical properties. It is essential to study the grinding effect and the parameters optimization, to achieve high efficiency and precision grinding. The response surface experiments of ZrO2 grinding are designed, where the grinding wheel speed, workpiece speed, and grinding depth are selected as the three grinding parameters. The experiment is carried out on a surface grinding machine with a diamond grinding wheel. The four response surface quadratic regression models are built for the grinding force, the surface roughness, the surface breakage rate, and the subsurface damage depth. Combined with the four regression models and the maximum undeformed chip thickness model, the effect of grinding parameters on the grinding results is comprehensively assessed. For the integrated minimum of the machining time and the four grinding results, the multi-objective optimization is carried out on the grinding parameters. The correctness of the optimization model is verified by experiment. [ABSTRACT FROM AUTHOR]

The rationality of product module partition is crucial to the success of modular design. The correlations between components of complex products are complex, increasing the difficulty of module partition. Thus, many existing methods of module partition have difficulty realizing this process effectively for complex products with a large number of components. This paper proposes a module partition method for complex products based on stable overlapping community detection and overlapping component allocation. The correlations between components are analyzed to obtain a comprehensive correlation strength matrix. The undirected weighted network is used to represent components and the correlations between them. A stable overlapping community detection algorithm based on the improved judgement of within-community Shapley values is proposed to generate multiple preliminary schemes of module partition. Overlapping components among modules are allocated to the most suitable modules by adopting a genetic algorithm (GA). The scheme with the largest modularity measure Q is selected as the final scheme of module partition. The proposed method is applied to a computer numerical control (CNC) grinding machine. The proposed module partition method for complex products is demonstrated to be superior to other effective methods. [ABSTRACT FROM AUTHOR]

Grind-hardening machining is a new integrated manufacturing technology that integrates the theory of material surface quenching and grinding machining. The surface integrity of grind-hardening directly affects the performance and reliability of the parts. Improving the grind-hardening quality has always been the focus and difficulty in this field. Based on the surface integrity theory and the characteristics of the grind-hardening process, this paper proposed four optimization criteria for grinding parameters according to the engineering application requirements of materials. Using the expectation function, the burr cross-sectional area, depth of the effective hardened layer, and surface roughness were comprehensively analyzed under each optimization criterion to obtain an optimal combination of grinding parameters. The results revealed a significant inconsistency in the optimized grinding parameters under each optimization criterion. When considering the depth of the effective hardened layer as the primary optimization parameter and ignoring the surface roughness and burr cross-sectional area, the highest overall desirability was 0.926395. In practical application, the optimization criteria should be reasonably selected according to the actual engineering requirements. [ABSTRACT FROM AUTHOR]

Ball mills are widely used for size reduction in mineral processing, but effective scaling from laboratory to industrial scale remains challenging. This study introduces a novel scaling constant approach to replicate energy transfer to ore during milling across different scales by adjusting rotational speed and grinding medium size distribution. The scaling constant encapsulates parameters like the number of balls per working area, rotational speed, and an average ball's maximum potential and kinetic energies. Experiments were conducted using a laboratory ball mill with interchangeable drum sizes (300, 400, and 500 mm) and a Design of Experiments methodology. Statistical analysis revealed that the scaling constant was more effective at maintaining consistent specific energy and energy per rotation across scales than size reduction, especially in dry milling. Wet milling results showed no significant differences in all metrics across scales. The dominant charge motion shifted from centrifuging to cascading as the mill diameter increased, highlighting the complex scaling dynamics. While the scaling constant shows promise for maintaining energy utilization, additional factors like charge motion and particle breakage mechanisms should be considered. The findings provide insights for improving ball mill design and optimization in mineral processing. [ABSTRACT FROM AUTHOR]

One of the inseparable components of the grinding process in tumbling mills is the grinding media used in them. Despite the simplicity of grinding media shapes in a tumbling mill, their effect on breakage performance as well as on the mill power draw has remained a complex problem. A large share of energy consumption in mineral processing plants is related to grinding operations, so the study of power consumption in this sector is very important. This study compares the effects of conventional rods (simple rods) and grooved rods on the mill power draw in a dry rod mill at various mill speeds and grinding media filling. The results of the study showed that with an increase in grinding media weight and mill speed, power consumption was increased for both types of grinding media studied. The specific power rate decreased with increasing the speed and media filling. By increasing mill speed, the sensitivity of the specific power draw to the grinding media filling was increased. The specific power draw of grooved rods at low and medium speeds was shown to be more than that of simple rods, but at speeds above 60% of the critical speed, the specific power draw of the grooved rods was lower than that of the simple rods. Using an empirical model, the experimental and model values were compared. The results showed that the prediction of the mill power draw using the presented empirical model, in the range of low and medium speeds is more reliable. Therefore, using this model at low speeds is perfectly valid. However, as speed increases, its credibility decreases. [ABSTRACT FROM AUTHOR]

The drive at the center of gravity (DCG) concept-based twin-ball-screw drive mechanism (TBSDM) is vital in automated factories for its robustness and reliability. However, changes in the worktable mass or position result in changes in the center of gravity (CG), significantly affecting the system's dynamic properties. In this regard, this paper introduces a novel analytical model using improved receptance coupling to analyze vibrations in four modes. A mathematical framework for the twin TBSDM is generated, and the effect of changing the worktable position–mass on each mode is examined. The applicability of the proposed method is verified based on dynamic experiments that were carried out on a TBSDM of a CNC grinding wheel machine tool. After thoroughly analyzing the experimental and theoretical results, it is revealed that changing the worktable position primarily influences the rotational and axial vibrations of the twin ball screw (TBS). Furthermore, changes in the worktable mass significantly affect the coupling vibration among the TBSs and rotors or bearings. Moreover, in terms of performance, the variances between the theoretical and experimental natural frequencies are consistently below 5%. Thus, the proposed method is promising for the improvement of the modeling and analysis of the TBSDM. [ABSTRACT FROM AUTHOR]

After grinding, the machine tool spindle with high surface integrity has a significant impact on its subsequent service life. Therefore, it is necessary to create a reasonable process plan for the grinding process of the machine tool spindle. A hybrid method based on case-based reasoning (CBR) and process reasoning (PR) was proposed in this paper. The subjective and objective weights of feature attributes are determined in the CBR by the AHP method and the CRITIC method, respectively, and the similarities between the latest case and the retrieved case are calculated on the basis of the nearest neighbour algorithm. The most suitable process solution can be selected from the case database by means of CBR. PR is applied to solve the problem that the case cannot be retrieved through CBR or is unsatisfactory. In the case of the grinding machine spindle, the applicability of this technology was demonstrated. Consequently, the results revealed that the surface consistency of the spindle after grinding was substantially increased. A decision-making system based on the proposed approach was developed by using Qt 4.8.7 and SQLite 3. The results demonstrate the viability and efficacy of the hybrid CBR-PR method to rapidly generate process planning for specific parts. [ABSTRACT FROM AUTHOR]

The production of non-circular thin optical lenses involves initial processes such as circular cutting, grinding, lapping, and polishing. However, shaping the lenses into non-circular forms requires a final cutting process. This study focuses on addressing the distortion of BK7 thin glass parts during side grinding. Two clamping methods were examined to fix the components. The first method, commonly used in the industry, involves bonding the workpiece to a base using resin and subsequent machining, followed by separating the samples from the base by heating the resin. In contrast, this research explores an alternative approach using vacuum clamping. The components' distortion was measured using an interferometer. The results show that the vacuum clamping method produces more uniform surface parameters for the polished components compared to the typical resin bonding method. Additionally, the analysis reveals that surface parameter variations are more pronounced during square side-grinding compared to circular side-grinding. [ABSTRACT FROM AUTHOR]

The reasonable allocation of geometric errors of machine tools can improve their machining accuracy reliability (MAR). However, due to the complexity and high nonlinearity of limit state function (LSF) of MAR, the fitting accuracy is usually low when the traditional method is used to approximate LSF. To solve this problem, a doubly-weighted vector projection response surface (DWVPRS) method, which considers not only the approximation results of test sample points (TSPs) to LSF but the distances between TSPs and the most probable failure point (MPFP), is proposed. Using the reliability sensitivity analysis method, the key geometric errors were identified and optimized. Finally, taking a large gantry guideway grinding machine as an example to verifies the effectiveness and correctness of the DWVPRS method proposed in this paper, the results show that compared with the traditional methods, the DWVPRS method has the highest fitting accuracy to approximate LSF at the MPFP, and after the optimization of geometric accuracy, both the minimum and average reliability values of the grinding machine meet the design requirements. [ABSTRACT FROM AUTHOR]

A method for dressing the worm wheel for grinding face gear with arbitrary modification amount by double cone dressing wheel is proposed. In order to ensure the machining accuracy and balance the machining time, the iteration method of tangent's deviation is proposed and the nominal residual mean error is defined. In order to improve the versatility of the scheme, a double cone dressing wheel and a universal worm grinding machine are used to carry out the research. Considering the complex space position transition, the principle of virtual phase transition is proposed to achieve the equivalent replacement of the motion of the double cone dressing wheel by the motion of the crown worm wheel, which consists of the principle of virtual tool setting and the principle of rotating virtual center distance. Finally, a simulation experiments is carried out on the dressing process of the modification crown worm wheel. The result shows that this method can effectively realize the modification dressing process of the crown worm wheel for grinding face gear. [ABSTRACT FROM AUTHOR]

The grinding process has been evolving, in recent years, from a mere surface finishing operation to a high stock removal process. The current research work is focused on the development of new-generation single-layer diamond wheels that can accomplish the demanding high material removal rate (MRR) requirements with longer tool life without any wheel dressing. With the use of a novel coordinate-controlled grit patterning technique, single-layer brazed diamond wheels were fabricated with uniform and precise distribution of the abrasive particles, which enables the wheels to accommodate higher chip load without loading and to simultaneously possess stronger joint strength. The indigenously developed grinding wheel was tested under aggressive grinding conditions to machine Al-SiCP composite blocks up to a depth of 2.5 mm under a flood cooling environment. Grinding forces, real-time spindle power consumption, chip morphology, and post-grinding wheel morphology were investigated during the deep grinding experiments to evaluate the suitability of such wheels in stringent grinding conditions. Experiments were conducted up to a maximum uncut chip thickness of 5.4 μm and maximum specific MRR of 20.83 mm2/s. Specific grinding energy, as low as 10.8 J/mm3, was obtained at the grinding condition with an aggressiveness number of 19.04. The in-house developed patterned diamond grinding wheel could successfully grind the soft, sticky, and abrasive Al-SiCP composites without any trace of wheel loading or significant grit failures. In the end, the indigenously developed patterned grinding wheel was compared against a commercially procured competent single-layer grinding wheel while grinding the same Al-SiCP composite in high-pressure cooling conditions. [ABSTRACT FROM AUTHOR]

Tolerance design of key components is an effective method to improve the machine tools accuracy. Currently, tolerance design highly depends on the personal experience, which often subjectively determines the machine tool accuracy and also increases the manufacturing costs. To avoid the phenomenon, a machining error prediction model (MEPM) assisting tolerance design is proposed and experimental verified based on a precision vertical grinding machine (PVGM). Firstly, the mapping relationship between the tolerance parameter and geometric error of key components is obtained as the superimposed Fourier series. Subsequently, the volumetric error model including geometric errors is established by the homogeneous transform matrix and multi-body system theory. Based on a typical workpiece, a tolerance-based MEPM is obtained by integrating the ideal machining path. Finally, a grinding test on PVGM is conducted to validate the MEPM. The results show that the predicted roundness of three positions is 3.033 μm, 2.905 μm, and 2.774 μm, respectively, and the measured roundness are 3.163 μm, 2.980 μm, and 2.904 μm, respectively. The error between PVGM predicted and measured roundness is not more than 5%. The MEPM is important for assisting the tolerance design and improving the machining accuracy in the machine tools. [ABSTRACT FROM AUTHOR]

Due to the complexity of the Wire-arc Additive Manufacturing (WAAM) process, it is prone to the occurrence of defects in the product. One of the most common defects is porosity, which is detrimental to the mechanical and fatigue properties of the product. To guarantee the product quality, it is important to detect its occurrence. In this research, a strategy was developed to detect the occurrence of porosity and to identify its root cause. To develop a porosity detection strategy, a monitoring set-up was designed in which the WAAM process is monitored by a current sensor, condenser microphone, structural Acoustic Emission sensor, spectrometer and flow meter. Seventeen experiment samples were produced and the sensor data were stored using a data acquisition system. A sensor data analysis showed that current and acoustic signals could be correlated with the occurrence of porosity. From these sensor signals, 74 features were extracted. A validation of the samples, based on milling, grinding and image processing, was developed to obtain the pore density values for the corresponding feature set. A long short-term memory neural network was trained through supervised learning to classify feature data into acceptable or non-acceptable pore densities. A test accuracy of 82.52% was achieved. Apart from the porosity detection, a strategy for porosity root cause detection was developed in order to distinguish between process related porosity and contaminant related porosity. An accuracy of 90.67% was achieved, whereby the remaining inaccuracy is interpreted as a physical phenomenon and the results from the model are interpreted actually as more accurate than the prior assumption of the training labels. [ABSTRACT FROM AUTHOR]

Nanofluids are efficient heat transfer media that have been developed over the past 27 years and have been widely used in the electronic microchannel, engine, spacecraft, nuclear, and solar energy fields. With the high demand for efficient lubricants in manufacturing, the application of nanofluids in machining has become a hot topic in academia and industry. However, in the context of the huge amount of literature in the past decade, existing review cannot be used as a technical manual for industrial applications. There are many technical difficulties in establishing a mature production system, which hinder the large-scale application of nanofluids in industrial production. The physicochemical mechanism underlying the application of nanofluids in machining remains unclear. This paper is a complete review of the process, device, and mechanism, especially the unique mechanism of nanofluid minimum quantity lubrication under different processing modes. In this paper, the preparation, fluid, thermal, and tribological properties of nanofluids are reviewed. The performance of nanofluids in machining is clarified. Typically, in friction and wear tests, the coefficient of friction of jatropha oil-based alumina nanofluids is reduced by 85% compared with dry conditions. The cutting fluid based on alumina nanoparticles improves the tool life by 177–230% in hard milling. The addition of carbon nanotube nanoparticles increases the convective heat transfer coefficient of normal saline by 145.06%. Furthermore, the innovative equipment used in the supply of nanofluids is reviewed, and the atomization mechanisms under different boundary conditions are analyzed. The technical problem of parameterized controllable supply system is solved. In addition, the performance of nanofluids in turning, milling, and grinding is discussed. The mapping relationship between the nanofluid parameters and the machining performance is clarified. The flow field distribution and lubricant wetting behavior under different tool-workpiece boundaries are investigated. Finally, the application prospects of nanofluids in machining are discussed. This review includes a report on recent progress in academia and industry as well as a roadmap for future development. [ABSTRACT FROM AUTHOR]

Due to the enclosed working environment and inadequate cooling, it is easy to produce excessive local temperatures in the double-face grinding machining process. Excessive local temperatures lead to a number of issues, including grinding wheel wear, workpiece burns, and surface quality degradation. In order to reduce the temperature with little coolant, a study on coolant distribution is necessary. This paper studies the effects of grinding wheel speed and workpiece speed on the coolant distribution law. The unidirectional flow field model in double-face grinding is established based on the contact equation of computational fluid dynamics, the Navier‒Stokes equation, and the turbulence equation. This model also considers the pores on the grinding wheel surface, the equivalent height of the abrasive particles, the geometric boundary, and the equivalent gap between the workpiece and the wheel. The results show that the component surface fluid velocity gradually rises from the inner to the outside diameter of the grinding wheel. The grinding area pressure distribution in different parameters is analyzed. The variance analysis of coolant distribution is carried out. It is found that there is a positive linear correlation between the grinding fluid flowing into the grinding zone and the grinding wheel speed. With the increase of wheel speed, the more liquid concentrates on the area where the coolant enters. And it is observed that 60 rad/s wheel speed leads to a more even coolant distribution and a flatter machined surface. [ABSTRACT FROM AUTHOR]

In the transition from virtual environments to real-world applications, the role of physics engines is crucial for accurately emulating and representing systems. To address the prevalent issue of inaccurate simulations, this paper introduces a novel physics engine uniquely designed with a compliant contact model designed for robotic grinding. It features continuous and variable time-step simulations, emphasizing accurate contact force calculations during object collision. Firstly, the engine derives dynamic equations considering spring stiffness, damping coefficients, coefficients of restitution, and external forces. This facilitates the effective determination of dynamic parameters such as contact force, acceleration, velocity, and position throughout penetration processes continuously. Secondly, the approach utilizes effective inertia in developing the contact model, which is designed for multi-jointed robots through pose transformation. The proposed physics engine effectively captures energy conversion in scenarios with convex contact surface shapes through the application of spring dampers during collisions. Finally, the reliability of the contact solver in the simulation was verified through bouncing ball experiments and robotic grinding experiments under different coefficients of restitution. These experiments effectively recorded the continuous variations in parameters, such as contact force, verifying the integral stability of the system. In summary, this article advances physics engine technology beyond current geometrically constrained contact solutions, enhancing the accuracy of simulations and modeling in virtual environments. This is particularly significant in scenarios wherein there are constant changes in the outside world, such as robotic grinding tasks. [ABSTRACT FROM AUTHOR]

Understanding the temperature–working condition relationship is crucial for optimizing machining processes to ensure dimensional accuracy, surface finish quality, and overall spindle longevity. Monitoring and controlling spindle temperature through appropriate cooling systems and operational parameters are essential for efficient and reliable machining operations. This paper presents an in-depth analysis of the thermal equilibrium and deformation characteristics of a high-speed motorized spindle unit utilized in grinding machine tools. Through a series of thermal equilibrium experiments and meticulous data acquisition, the study investigates the nuanced influence of various working conditions, including spindle speeds, coolant types, and coolant flow rates, on spindle temperatures and thermal deformations. Leveraging the power of Artificial Neural Networks (ANNs), predictive models are meticulously developed to accurately forecast spindle behavior. Subsequently, the models are seamlessly transitioned to a cloud computing infrastructure to ensure remote accessibility and scalability, facilitating real-time monitoring and forecasting of spindle performance. The validity and reliability of the predictive models are rigorously assessed through comparison with experimental data, demonstrating excellent agreement and high accuracy in forecasting spindle thermal behavior. Furthermore, the study underscores the critical role of key working condition variables as precise predictors of spindle temperature and thermal deformation, emphasizing their significance in optimizing overall spindle efficiency and performance. This comprehensive analysis offers valuable insights and practical implications for enhancing spindle operation and advancing the field of grinding machine tools. [ABSTRACT FROM AUTHOR]

Granite edge finishing through grinding is a common process in the granite processing industry, crucial for achieving the final desired shape and edge quality of products. This study focuses on the granite industry, specifically delving into the significance of grinding and polishing for improving aesthetics and extending material longevity. The experimental design entails a comprehensive factorial experiment plan involving two workpiece materials (white and black granite samples) and two cutting tool edge shapes (chamfer and concave), each with two grit sizes: G150 and G600. The cutting conditions varied and consisted of variations in spindle speeds (1500, 2500, 3500 rpm), feed rates (500, 1000, 1500 mm/min), and lubrication modes (wet/dry). The results uncover intricate relationships among these parameters and part quality, underscoring the pivotal role of tool geometry in achieving superior surface finishes and in controlling the cutting forces. These findings contribute to a nuanced understanding of the dynamic interplay between tool characteristics, material properties, and machining conditions within the granite industry. [ABSTRACT FROM AUTHOR]

A ball mill with a diameter of 305mm and a length of 150mm was used as the experimental object, the original movement data of grinding media at 60%, 70% and 80% of the critical rotational speeds were obtained by using the detection ball device. By using the attitude angle algorithm to eliminate the gravity factor in the original data, data obtained are closer to the real motion data. The experimental results show that when the rotation speed is 60%, the average value of the original force is 1.717 N, the average value of the true data force is 1.990 N, the average angular velocity is 530.1 deg/s, and the standard deviation is 221.15. When the rotation speed is increased to 70%, the rotation speed is increased to 70%, the average value of the original force is 1.796 N, the average value of the true data force is 2.079 N, the average angular velocity is 628.9 deg/s, and the standard deviation is 238.3. However, the average value of the original force decreases to 0.826 N at 80% speed, the true data force average is 1.377 N, the mean angular velocity is 656.3 deg/s, and the standard deviation is 262.55. According to these results, the following conclusions can be drawn: 1) The true data force average value of grinding medium is larger than the original force average value. 2) With the increase of the rotating speed of the ball mill, the impact force on the grinding medium increases at first and then decreases. 3) With the increase of ball mill speed, the angular velocity of the test ball increases continuously. Because the angular velocity may affect the grinding effect of grinding medium, it shows that the cylinder can provide more energy to the grinding medium and bring better grinding effect. 4) With the increase of the ball mill speed, the standard deviation of the angular velocity of the test ball also increases continuously, which shows that the fluctuation range of the kinetic energy of the grinding medium is larger and the frequency of the energy exchange between the media is increased under the high rotating speed, the grinding effect of grinding medium can be brought into play more obviously. [ABSTRACT FROM AUTHOR]

Brazed carbide profile mill cutters are widely used in the machining of wood, wood materials, and plastic to form various functional and aesthetic surfaces. Tools of this kind are used in serial and mass production and they are built today using very costly 5-axis grinding machines supported by specialized CAM software that is very expensive too. The paper first developed the possible concept of 5-axis grinding of brazed profile mill cutters using universal diamond grinding wheel shapes. The developed concept of 5-axis grinding served as a basis for the developed approach for 3+2-axis grinding of the brazed form mill cutters which is the essence of this paper. Verification of this set approach of 3+2-axis grinding is performed on a developed functional prototype of a simple 3+2-axis grinder (Axes XYZ are CNC controlled, axes B and C are unpowered axes and have fixed positions during grinding). Based on the established 3+2 grinding strategy complete grinding of a complex profile has been performed on a developed functional prototype. Shape and measures of the cutting edge profile achieved by grinding are inspected on an optical measuring system and showed exceptional results. The established 3+2-axis method of grinding is an economically successful alternative to costly 5-axis grinding machines, as well as to specialized software, presented in the paper through experimental and practical application. [ABSTRACT FROM AUTHOR]

To achieve a fundamental reduction in the carbon emissions associated with grinding machines, it is imperative to systematically explore low-carbon considerations in the design phase. The spindle is a significant contributor to carbon emissions in grinding machines, and an effective approach for reducing carbon emissions is the structural optimization of the spindle. Most of the current optimization methods aim at improving processability without considering the reduction of carbon emissions. In this context, the present study addresses the issue of carbon emissions within the spindle design phase. Initially, the determination of the spindle's carbon emissions function and the selection of the optimization objective were undertaken. The structural factors that have a significant influence on the optimization objective were identified as optimization variables. Subsequently, the optimization objective function was established through the application of the fitting method. Finally, the proposed model was refined through the utilization of an enhanced whale algorithm. The findings indicate an 8.22% reduction in carbon emissions associated with the spindle, accompanied by marginal enhancements in both static and dynamic spindle performance. The concluding section of this paper deliberates on the impact of structural parameters on the specified objectives, thereby providing insights for the optimal design of the spindle. [ABSTRACT FROM AUTHOR]

Grinding is widely used for machining high-precision parts, but grinding wheel wear and grinding wheel vibration significantly impact the surface quality of the parts in the process of machining. However, the effect of those two factors on the grinding surface has not been considered simultaneously in most grinding surface topography simulation studies. Hence, the paper conducts a simulation study of grinding surface topography with the consideration of the two factors. First, the actual topography of the grinding wheel surface is measured, and the surface topography of the grinding wheel is modeled accordingly. Second, the kinematic analysis of grinding is carried out and a mathematical model of grinding surface topography simulation, which involves grinding wheel wear and grinding wheel vibration, is constructed. Finally, grinding machining experiments are conducted to compare and analyze the surface topography and surface roughness of the actual and simulated grinding surfaces. The results show that the average relative errors of the four roughness parameter values between the simulated and actual grinding wheel surfaces are all below 2.5%. With the consideration of those two factors, the average relative errors of the surface roughness parameter values between the actual grinding surface and the corresponding simulated grinding surface are all less than 13.5%. This verifies the correctness and effectiveness of the method proposed in this paper. Improving the accuracy of the simulated grinding surface topography allows for better optimization of the selection of grinding machining parameters. It helps to precisely predict the surface topography and surface roughness of the grinding surface. [ABSTRACT FROM AUTHOR]

A Monte Carlo simulation method based on multisource bayes is proposed to improve the reliability of motorized spindles in cycloid gear grinding machines and reduce their failure rate. Based on field investigations and motorized spindle maintenance records, a fault tree model of a motorized spindle was established, and the fuzzy importance of each bottom event was evaluated. The fault tree of a motorized spindle was used as a Monte Carlo reliability simulation model, and its importance was used as the input parameter for the simulation. The reliability evaluation index of the motorized spindle was obtained at different simulation times. The feasibility and accuracy of the reliability simulation were verified by comparing the importance and simulation importance. A vibration test was designed for bearing faults with high importance, and fault extraction was performed by combining the wavelet packet transform and empirical mode decomposition. This method can also be used to simulate and analyze the reliability of other equipment or machine tools. [ABSTRACT FROM AUTHOR]

This study introduces a new method for efficient and low-damage machining of Ti6Al4V: laser-assisted CBN belt grinding. The impact of laser power and grinding depth on the machining behavior of the material is investigated, the elemental distribution and chemical state of the ground surface are analyzed, and the removal mechanism of laser-assisted CBN belt grinding is described. The results show that increasing laser power will improve processing efficiency, enhance plastic material removal, and improve surface quality; the tangential grinding force is decreased by 63.5% and the normal grinding force is decreased by 72.6% at the maximum laser power; a large amount of Ti and N are present on the ground surface, and they are present in the form of TiN compounds. This study is a guide to the research on efficient and low-damage machining of hard-to-process materials like titanium alloys. [ABSTRACT FROM AUTHOR]

The global supply chain depends on the efficient transfer of containers between cargo ships and ground transport. To ensure reliable operations, equipment at ports requires predictive maintenance, with minimal downtime, at scheduled times. One common piece of equipment, rail mounted automated gantry (RMG) automatic stacking cranes (ASC), can be outfitted with commodity acceleration sensors to better monitor the structural health of the crane and the rails on which the crane runs. The ratio of the rolling standard deviation of the crane vibration at speed versus at rest is a metric that can separate the cranes and rails into those that are in need of detailed inspection and repair, and those that are in good health. This method produced zero false negative, and an even split of true positives and false positives. The cost of a false positive is orders of magnitude less than the cost of a false negative, so this technique should help bend the cost curve down on RMG maintenance. Industrial RMG ASCs and the rails on which they run are difficult and expensive to survey, but abnormalities can cause damage to the cranes that will be much more expensive and difficult to fix. We are developing a method using low cost computing in the form of Raspberry Pis and commodity acceleration sensors to detect abnormalities without disturbing the use of the crane for a survey. One limitation of low cost sensors is that they produce a signal with a relatively high amount of noise. However, the signal is usable. We quantify the noise level as the rolling standard deviation (RSD) of the raw signal from the smoothed signal, which can be calculated for the crane at rest and at different speeds. The ratio of resting RSD to moving RSD is used to classify a rail as needing attention or as nominal. That ratio can be compared between two cranes that are operating on the same set of rails to determine whether the abnormalities are caused by the crane or by the rail. Utilizing this metric to prioritize track repairs such as tamping or grinding, or crane repairs such as wheel replacement or bogie alignments can significantly reduce the maintenance cost for the overall crane systems. [ABSTRACT FROM AUTHOR]