Search Results (63)

For the problem of online real-time prediction of copper particle content in the lubricating oil of the main spindle-bearing system of mining equipment, the traditional direct detection method is costly and has insufficient real-time performance. To this end, this paper proposes an indirect prediction method based on data-driven neural networks. The proposal of this method is based on a core assumption: during the stable wear stage of the equipment, there exists a modelable statistical correlation between the copper particle content in the oil and the total amount of non-ferromagnetic particles that are easy to measure online. Based on this, a neural network prediction model was constructed, with the online metal abrasive particle sensor signal (non-ferromagnetic particle content) as the input and the copper particle content as the output. The experimental data are derived from 100 real oil samples collected on-site from the lubrication system of the main shaft bearing of a certain mine mill. To enhance the model’s performance in the case of small samples, data augmentation techniques were adopted in the study. The verification results show that the average prediction accuracy of the proposed neural network model reaches 95.66%, the coefficient of determination (R²) is 0.91, and the average absolute error (MAE) is 0.3398. Its performance is significantly superior to that of the linear regression model used as the benchmark (with an average accuracy of approximately 80%, R² = 0.71, and the mean absolute error (MAE) = 1.5628). This comparison result not only preliminarily verified the validity of the relevant hypotheses of non-ferromagnetic particles and copper particles in specific scenarios, but also revealed the nonlinear nature of the relationship between them. This research explores and preliminarily validates a low-cost technical path for the online prediction of copper particle content in the stable wear stage of the main shaft bearing system, suggesting its potential for engineering application within specific, well-defined scenarios. Full article

The development of modern machining and manufacturing industry puts forward higher requirements for the machining accuracy of machine tools. The thermal error of the machine tool spindle directly affects the accuracy of the machined workpiece. To improve the accuracy of thermal error prediction, this paper conducts temperature field analysis for the thermal error of the machine tool spindle and employs the Whale Optimization Algorithm (WOA) to optimize the temperature field parameters, aiming to establish a spindle temperature field model. This approach avoids the problem that traditional measurement methods cannot obtain the temperature of key rotational positions of the spindle and provides a new method for the selection of temperature-sensitive points in the thermal error measurement process. Initially, a spindle Product of Exponentials (POE) error model is constructed to map the five errors of the spindle to three-dimensional vectors in the machine tool space. Subsequently, the Whale Optimization Algorithm (WOA) is used to optimize the physical parameters of the spindle, and the optimal spindle temperature field model is determined. The calculated spindle thermal error data and temperature field model data are input into the OLGWO-SHO-CNN model for training. Finally, a case study is carried out on a machining center, and the trained model is used to perform compensation verification under constant and variable speed conditions, respectively. The experimental results show that under the constant speed condition, the compensation rates of the X-axis, Y-axis, and Z-axis are 77.2%, 73.1%, and 88.7%, respectively; under the variable speed condition, the compensation rates of the X-axis, Y-axis, and Z-axis are 74.7%, 78.2%, and 88.0%, respectively. The compensation results indicate that the established spindle temperature field model and the OLGWO-SHO-CNN model have good robustness and accuracy. Full article

Thermal elongation in high-speed motorized spindles constitutes a major source of machining error in five-axis machine tools, critically impacting machining precision. This study aims to develop and validate a cumulative thermal error compensation model for predicting spindle thermal elongation, subsequently enabling effective compensation via a dedicated control algorithm. Key thermal error factors, primarily spindle speed and cumulative thermal error, were identified through analysis. An innovative numerical prediction model incorporating these factors was established. Its performance was evaluated through experiments utilizing eddy-current displacement sensors for high-speed, high-precision thermal elongation measurement. The validation results demonstrated the model’s strong predictive capability: During spindle startup, prediction errors exhibited minor transients, stabilizing near zero once the operating speed was reached. Under dynamic speed changes, the maximum prediction error was only 1.28 μm, with the overall maximum residual error recorded at 2.04 μm. These findings confirm the model’s high accuracy. Furthermore, the model exhibits excellent generalization capability, delivering significant compensation effectiveness across diverse variable-speed operating conditions. This work successfully developed a highly accurate numerical model and a practical compensation strategy, significantly enhancing the positioning accuracy of high-speed spindles against thermal disturbances. The proposed approach offers substantial engineering utility for thermal error compensation in precision machining applications. Full article

The thermal deformation of high-precision CNC machine tools has long been a significant barrier to improving machining accuracy. Accurately characterizing the thermal properties of the spindle, especially the convective heat transfer coefficients (CHTC), is essential for precise thermal analysis. However, due to the lack of dedicated instruments for directly measuring the CHTC, thermal analysis of the spindle faces substantial challenges. This study presents an innovative approach that combines multi-sensor data with intelligent optimization algorithms to address this issue. A distributed temperature monitoring network is constructed to capture real-time thermal field data across the spindle. At the same time, an improved Grey Wolf Optimization (IGWO) algorithm is employed to dynamically and accurately identify the CHTC. The proposed algorithm introduces an adaptive weight adjustment mechanism, which overcomes the limitations of traditional optimization methods in dynamic operating conditions. Experimental results show that the proposed method significantly outperforms conventional approaches in terms of temperature prediction accuracy across a broad operating range. This research provides a novel technical solution for machine tool thermal error compensation and establishes a scalable intelligent indirect measurement framework, even in the absence of specialized measurement instruments. Full article

Cylindrical gears are used extensively due to their significant advantages including high efficiency, high load-bearing capacity, and long lifespan. However, the machining accuracy of cylindrical gears is significantly affected by motion errors and force-induced errors of machine tools. In this study, a motion error model of the machine tools was established based on multi-body system theory and homogeneous coordinate transformation method, quantifying the contributions and variation patterns of 12 key errors in the A and B-axes to workpiece geometric errors. Then, by using the stiffness analytical model and the spatial meshing theory, the influence of the force-induced elastic deformation of the shaft of rolling wheel and the springback of the workpiece tooth flank on the geometric error was revealed. Finally, taking the through rolling of a spur cylindrical gear with a module of 1.75 mm, a pressure angle of 20°, and 46 teeth as an example, the force-induced elastic deformation model of the shaft was verified by the rolling tests. Results show that for 40CrNiMo steel, the total profile deviation, total helix deviation, and single pitch deviation in the X-direction caused by rolling forces are 32.48 μm, 32.13 μm, and 32.13 μm, respectively, with a maximum contact rebound is δ_c = 28.27 μm. The relative error between theoretical and measured X-direction spindle deformation is 8.26%. This study provides theoretical foundation and experimental support for improving the precision of rolling process. Full article

The spindle motion error significantly affects the surface quality and dynamic precision of machined workpieces. This study proposes a novel detection method for workpiece surface morphology with arbitrary rotation angles. A mathematical model was established for the relationship between the detection signal, spindle error, and workpiece contour when the workpiece rotates at different angles. Unlike traditional reversal methods, this approach allows a flexible selection of workpiece rotation angles and simplifies calculations. Simulation results demonstrate the method’s accuracy, with the slight mean square errors and determination coefficients R² approaching 1. Experimental validation confirms the method’s reliability. Furthermore, the influences of asynchronous errors and sensor errors on measurement results were systematically investigated, highlighting the importance of increasing sampling periods and accurate positioning of sensors. This method offers a cost-effective and versatile solution for precision machining and can be extended to other rotating machinery applications. Full article

As the core component of ultra-precision machine tools, the manufacturing errors of aerostatic spindles are inevitable due to the limitations of machining and assembly processes, and these errors significantly affect the spindle’s static and dynamic performance. To address this issue, a force model of the unbalanced air film, considering the straightness errors of the rotor’s radial and thrust surfaces, was constructed. Unlike conventional studies that rely solely on idealized error assumptions, this research integrates actual straightness measurement data into the simulation process, enabling a more realistic and precise prediction of bearing performance. Rotors with different tolerance specifications were fabricated, and static performance simulations were carried out based on the measured geometry data. An experimental setup was built to evaluate the performance of the aerostatic spindle assembled with these rotors. The experimental results were compared with the simulation outcomes, confirming the validity of the proposed model. To further quantify the influence of straightness errors on the static characteristics of aerostatic spindles, ideal functions were used to define representative manufacturing error profiles. The results show that a barrel-shaped error on the radial bearing surface can cause a load capacity variation of up to 46.6%, and its positive effect on air film load capacity is more significant than that of taper or drum shapes. For the thrust bearing surface, a concave-shaped error can lead to a load capacity variation of up to 13.4%, and its enhancement effect is superior to those of the two taper and convex-shaped errors. The results demonstrate that the straightness errors on the radial and thrust bearing surfaces are key factors affecting the radial and axial load capacities of the spindle. Full article

The spindle is a critical component that significantly influences the performance of machine tools. In motorized spindles, heat generation from both the bearings and built-in motor leads to thermal deformation of structural components, which, in turn, affects machining accuracy. This study investigates the thermo-mechanical behavior of motorized spindles under various operational conditions, with the aim of accurately predicting thermally induced axial deformation and determining optimal temperature sensor placement. To achieve this, temperature rise and deformation data were simultaneously collected using appropriate data acquisition systems across varying spindle speeds. A correlation analysis confirmed a strong positive relationship exceeding 97.5% between temperature rise at all sensor locations and axial thermal deformation. Multivariate regression analysis was then applied to identify optimal combinations of sensor data for accurate deformation prediction. Additionally, a finite element (FE) thermal–mechanical model was developed to simulate spindle behavior, with the results validated against experimental measurements and regression model predictions. The four-variable regression model and FE simulation achieved Root Mean Square Errors (RMSEs) of 0.84 µm and 0.82 µm, respectively, both demonstrating close agreement with experimental data and effectively capturing the trend of thermal deformation over time under different operating conditions. Finally, an optimal sensor configuration was identified that minimizes pre-diction error while reducing the number of required sensors. Overall, the proposed methodology offers valuable insights for optimizing spindle design to enhance thermal–mechanical performance. Full article

To address uneven airflow distribution and pesticide deposition coverage in orchard pesticide application, we developed a double-flexible duct spraying device. Utilizing FLUENT 2022 software for airflow field simulation, we analyzed various structural parameters to identify optimal configurations for the air duct type, diameter, and nozzle outlet diameter. The results indicated that the nozzle outlet diameter most significantly influences wind field uniformity, followed by the air duct diameter and type. The optimal settings were identified as follows: C-Type air duct, 100 mm duct diameter, and 50 mm nozzle outlet diameter. Validation tests confirmed these settings, with simulated and actual wind speed measurements, showing no more than a 10% relative error, affirming the simulation’s accuracy. Field tests demonstrated an average droplet density of 35.38 droplets/cm² within tree canopies, indicating strong penetration ability. Droplet distribution followed a lower > middle > upper pattern in the canopy’s vertical direction, fulfilling technical requirements for high spindle-shaped fruit trees and providing a foundation for achieving a uniform canopy coverage. 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

Burrs generated during the drilling of carbon fiber-reinforced plastics (CFRPs) would seriously reduce the service life of the components, potentially leading to assembly errors and part rejection. To solve this issue, this paper proposed a finite element (FE) model with multiscale modeling to investigate the formation and distribution of burrs at various processing conditions. The FE model comprised the microscopic fiber and resin phases to predict the formation process of burrs, while part of the CFRP layers was defined to be macroscopic equivalent homogeneous material (EHM) to improve the computational efficiency. A progressive damage constitutive model was proposed to simulate the different failure modes and damage propagation of fibers. The impact of strain rate on the mechanical properties of the resin and CFRP layers was considered during the formulation of their constitutive models. With this numerical model, the formation process of the burrs and the drilling thrust force were accurately predicted compared to the experimental measurements. Then, the burr distributions were analyzed, and the influences of the drill bit structures and drilling parameters on burrs were assessed. It was concluded that the burrs were easily generated in the zones with 0° to 90° fiber cutting angles at the drilling exit. The sawtooth structure could exert an upward cutting effect on burrs during the downward feed of the tool; thus, it is helpful for the inhibition of burrs. More burrs were produced with higher feed rates and reduced spindle speeds. Full article

This study expands on prior research by generalising the machine tool integrated inverse multilateration methodology to evaluate ambient thermal effects on medium- and large-sized machine tools in industrial environments. This method integrates an absolute distance measurement device into the machine tool spindle, enabling an automated and robust multilateration scheme without requiring controlled environments, expensive thermal instruments, or specialised artifacts. Tests were conducted using a LEICA AT960™ laser tracker and wide-angle retro-reflectors (both from Hexagon Manufacturing Intelligence, Stockholm, Sweden) across two machine architectures, THERA™ (gantry type) and ZERO™ (bed type), building on earlier work with the ARION G™ (bridge type), all of them MTs manufactured by Zayer (Vitoria, Spain). Sequential experiments in varying ambient conditions demonstrated the reliability of the machine tool integrated inverse multilateration approach over extended periods, showing strong correlations between the measured errors and temperature variations. The results were validated using a first-order mathematical model and finite element method simulations, confirming thermal error evolution as a function of ambient temperature changes. This method’s adaptability to diverse machine architectures and industrial conditions highlights its potential for characterising and mitigating thermal errors in large machine tools. This work underscores the method’s effectiveness and utility for advancing thermal error analysis in practical manufacturing settings. Full article

In CNC machines, the temperature field analysis of spindle servo permanent magnet motors (SSPMMs) under rated load, overload, and weak magnetic conditions is critical for ensuring stable operation and machining accuracy. This paper proposes a temperature calculation method for SSPMMs based on the thermal network method, which is used to quickly evaluate the temperature performance of SSPMMs under different operating conditions during design. This method can calculate the steady-state or transient temperature rise under different operating conditions. First, the electromagnetic performance and heat sources of the SSPMMs were analyzed. Then, based on the thermal network method, the equivalent thermal resistances and equivalent heat dissipation coefficients of the motor components were calculated. By iterating the heat balance equation or solving the heat conduction equation for different operating conditions, the temperature distribution of SSPMMs under different operating conditions was obtained. The accuracy of the thermal network model was validated through temperature analysis using fluid–structure interaction simulations and prototype testing. The results show that the relative error between the winding temperature calculated by the proposed equivalent thermal network model and the measured temperature under different operating conditions is less than 5%. This paper provides a theoretical basis for the thermal management of SSPMM, which can quickly and accurately evaluate the temperature rise in the motor during design. Full article

Optimizing energy consumption in machining processes is critical for achieving sustainable manufacturing. This study introduces an Automation-Supported measurement approach that integrates a custom power analyzer with real-time data logging and visualization capabilities to accurately measure energy usage during CNC (computer numerical control) operations. Statistical comparisons were conducted using the independent samples t-test and Taguchi analysis to evaluate the effectiveness of the proposed method against traditional measurement techniques. The results revealed that there is a statistically significant difference (p < 0.05) in the current measurements across X, Z, and spindle motors between the proposed and conventional methods. The advanced method based on automation reduced the error rate in measuring spindle motor power consumption due to the selection of processing parameters from 34.17% to 2.7%. Additionally, Taguchi analysis demonstrated that the measurement method influenced the optimization of machining parameters, with S/N ratio improvements observed. These findings confirm that the proposed method enhances energy efficiency, reduces environmental impact, and supports sustainable manufacturing practices. Full article

The surface roughness of hole machining greatly influences the mechanical properties of parts, such as early fatigue failure and corrosion resistance. The boring and trepanning association (BTA) deep hole drilling with axial vibration assistance is a compound machining process of the tool cutting and the guide block extrusion. At the same time, the surface of the hole wall is also ironed by the axial large amplitude and low-frequency vibration of the guide block. The surface-forming mechanism is very complicated, making it difficult to obtain an effective theoretical analytical model of the surface roughness of the hole wall through kinematic analysis. In order to achieve accurate prediction of the surface quality of the hole wall, the chip-breaking mechanism and the hole wall formation mode of BTA deep hole vibration drilling were analyzed. The influence of drilling spindle speed, feed, amplitude, and vibration frequency on the surface roughness of the hole wall during BTA deep hole vibration drilling was illustrated by a single-factor experiment. A four-factor and three-level test scheme was designed by using the Box–Behnken design (BBD) experimental design method. A surface roughness prediction model for hole wall machining was established based on the response surface methodology. The accuracy of the prediction model was analyzed through ANOVA, and the complex correlation coefficient of the model was 0.9948, indicating that the prediction model can better reflect the mapping relationship between vibration drilling parameters and surface roughness. After optimization analysis and experimental verification, the obtained vibration drilling parameters can achieve smaller surface roughness. The error between the predicted value of the model and the experimental measurement value is 8.65%. The established prediction model is reliable and can accurately predict the surface roughness of the hole wall of BTA deep hole axial vibration drilling, providing a theoretical basis for the surface quality control of the machining hole wall. It can be applied to process optimization in practical production. Full article