Search Results (39)

To address the problems of insufficient precision reserve, limited rotational speed, and excessive temperature rise in high-speed ultra-precision machine tool spindle bearings, the influence of frictional power loss on the thermo-mechanical behavior of the bearing system was investigated. Firstly, based on the analysis of the heat source of the bearing, the friction power consumption model of the bearing assembly is established, and the analysis of the bearing temperature field is realized by studying the heat energy transfer. Secondly, the test bench is built for experimental verification. Finally, through the study of thermal-mechanical coupling performance, the influence of different rotational speeds on bearing stress and life is analyzed. The results show that the friction power consumption generated by the spin sliding of the bearing rolling element accounts for the largest proportion, accounting for 31% of the total friction power consumption; the increase in bearing speed will increase the bearing temperature. At 55,000 r/min, the highest temperature at the rolling element is close to 75 °C, followed by the inner ring up to 68 °C, and the lowest outer ring temperature is 57 °C. The temperature has a great influence on the bearing performance. Under the same working conditions, the equivalent stress is increased by 21%, the contact pressure is increased by 25%, and the fatigue life of the bearing is reduced by 5.6%. Bearing performance is significantly affected by thermodynamic behavior. Full article

This work presents an investigation of two cooling systems used in additive friction stir depositions (AFSD) and the related effect on process temperature, feed material, and life of processing equipment. A new AFSD cooling system, Mazak MegaStir Liquid-cooled Toolholder (LCTH), was integrated onto MELD Manufacturing AFSD machines using both continuous and discrete material feeding systems. The LCTH is compared to the original MELD cooling system to understand how process temperatures are controlled by the cooling systems, how feed material interacts with the process under different cooling conditions, and how well the cooling systems protect the equipment. It is shown that spindle revolution rate impacts the temperature experienced by the bearings more greatly than the utilization of cooling. The attempt of three large volume depositions demonstrates the necessity of a cooling system utilization for AFSD. Additionally, four configurations of tool and cooling system are analyzed to understand parameters that affect depositions temperature and the thermal effect on feed material. Configurations with short working face to cooling distances show ~40 K cooler deposition temperatures and configurations with the new LCTH deposited ~9 K cooler than those deposited with the original MELD cooling system. Microhardness analysis on feed material revealed the varying efficacy of each cooling configuration’s ability to enable bulk deposition. The influence of feed system on process temperatures is also presented with a comparison of discrete and continuous feed system depositions. Full article

Rolling bearings are crucial components in CNC machine tool spindles, and their health condition directly affects machining precision and operational reliability. To address the significant challenges of bearing fault diagnosis in industrial environments, this paper proposes an adaptive shapelet-based deep learning model for bearing fault diagnosis. The proposed model integrates three key components: (1) an adaptive multi-scale shapelet extraction module for discriminative pattern learning, (2) a gated parallel CNN with depthwise separable convolutions for multi-scale spatial feature extraction, (3) an enhanced bidirectional long short-term memory network with residual connections for temporal dependency modeling. A composite loss function combining cross-entropy, supervised contrastive learning, and multi-scale consistency regularization is employed for training. To simulate real-world industrial noise conditions, Gaussian, uniform, and impulse noise were injected into the signals. Experiments conducted on the CWRU and IMS datasets demonstrate that, compared with state-of-the-art methods, the proposed approach achieves stronger noise robustness, higher fault classification accuracy, and more stable performance under severe noise contamination. Full article

Tapered roller bearings are widely employed in mechanical structures such as automotive wheel hub units, transmissions, and machine tool spindles, and they have a direct impact on the performance and stability of the equipment. The shape error and surface quality of the bearing raceway, as its working interface, directly affect its service performance. Grinding is an important process in a machining bearing raceway, and the formed roundness error and surface roughness of a raceway affect the workload of subsequent precision polishing processes. In order to reveal the effect of workpiece rotational speed, grinding wheel linear velocity, and grinding depth on the machining quality of the bearing outer ring inner raceway, single-factor experiments and surface roughness orthogonal experiments were conducted. The results were analyzed for range and variance using surface roughness Ra as the evaluation index, and we developed a mathematical model using a regression method for Ra. It has been found that the roundness error and surface roughness of the bearing raceway are improved with the increase in the grinding wheel linear velocity and the decrease in the grinding depth and workpiece rotational speed. The grinding depth has the greatest impact on surface roughness and the most significant effect. Next are the grinding wheel linear velocity and the workpiece rotational speed, while the effect of changes in workpiece rotational speed on roughness is relatively insignificant. The lowest surface roughness obtained under the optimized grinding parameter combination is 0.205 μm. Full article

Monitoring spindle bearing load is essential for ensuring machining accuracy, reliability, and predictive maintenance in machine tools. This paper presents an approach that combines drive-based cutting force estimation with a finite element method (FEM) spindle model. The drive-based method reconstructs process forces from the motor torque signal of the feed axes by modeling and compensating motion-related torque components, including static friction, acceleration, gravitation, standstill, and periodic disturbances. The inverse mechanical and control transfer behavior is also considered. Input signals include the actual motor torque, axis position, and position setpoint, recorded by the control system’s internal measurement function at the interpolator clock rate. Cutting forces are then calculated in MATLAB/Simulink and used as inputs for the FEM spindle model. Rolling elements are replaced by bushing joints with stiffness derived from datasheets and adjusted through experiments. Force estimation was validated on a DMC 850 V machining center using a standardized test workpiece, with results compared against a dynamometer. The spindle model was validated separately on a MCV 754 Quick machine under static loading. The combined approach produced consistent results and identified the front bearing as the most critically loaded. The method enables practical spindle bearing load estimation without external sensors, lowering system complexity and cost. 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

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

This work introduces an end-to-end methodology, from data gathering to fault notification, for the predictive maintenance of rotary components of machine tools. This is done through fingerprint routines; that is, processes that are executed periodically under the same no-load conditions to obtain a snapshot of the machine condition. High-frequency vibration data gathered during these routines combined with knowledge about the machine structure and its components are used to obtain failure-specific features. These features are then introduced to an anomaly and paradigm shifts detection algorithm. The method is evaluated through three distinct scenarios. First, we use synthetically generated data to test its ability to detect controlled variations and edge cases. Second, we use with publicly available data obtained from bearing run-to-failure tests under normal load conditions on a specially designed test rig. Finally, the methodology is validated using real-world data collected from a spindle bearing installed in a machine tool. The novelty of this work lies in performing anomaly detection using failure-specific features derived from fingerprint routines, ensuring stability over time and enabling precise identification of machine conditions with minimal data requirements. Full article

Machine tool vibrations play a significant role in hindering productivity during machining. The growing vibrations accelerate tool wear and chipping, cause a poor wave surface finish, and may damage the spindle bearing. Some research showed that tribological properties such as friction factors can have obvious influences on the topography of rough surfaces and the nonlinear dynamic characteristics of machine tool systems. Therefore, studying the vibration friction dynamic characteristics on the normal contact stiffness (NCS) of joints of CNC machine tools is absolutely necessary for improving the machining accuracy and precision of the whole system. The study results of NCS of joints of the CNC and the friction coefficient are discussed in this paper. The model of NCS based on fractal parameters was obtained. The models of deformations of the rough surfaces and contact surfaces were deduced. The results showed that the NCS based on the calculation method considering the elastic–plastic deformation of the asperity is much higher in precision than the methods considering only elastic or plastic deformation separately. The observations this paper described suggest that in the CNC machine tools system, higher D and G and higher friction coefficients lead to higher normal contact stresses (NCSs). Full article

In response to the challenge of timely fault identification in the spindle bearings of machine tools operating in complex environments, this study proposes a method based on a combination of infrared imaging with an Informer and a CNN + Swin Transformer. The aim is to achieve real-time monitoring of bearing faults, precise fault localization, and classification of fault severity. To accomplish this, an angular contact ball bearing was chosen as the research subject. Initially, an infrared image dataset was constructed, encompassing various fault positions and degrees, by simulating different forms of bearing faults. Subsequently, an Informer-based bearing temperature prediction model was established to select faulty bearing data. Lastly, the faulty data were input into the CNN + Swin Transformer model for bearing fault recognition and classification. The results demonstrate that the Informer model accurately identifies abnormal temperature rises during bearing operation, effectively screening out faulty bearings. Under steady-state conditions, the model achieves a classification accuracy of 97.8%. Furthermore, after employing the Informer screening process, the proposed model exhibits a recognition precision of 98.9%, surpassing other models such as CNN, SVM, and Swin Transformer, which are mentioned in this paper. Full article

The spindle tool is an important module of the machine tool. Its dynamic characteristics directly affect the machining performance, but it could also be affected by thermal deformation and bearing preload. However, it is difficult to detect the change in the bearing preload through sensory instruments. Therefore, this study aimed to establish a digital thermal–mechanical model to investigate the thermal-induced effects on the spindle tool system. The technologies involved include the following: Run-in experiments of the milling spindle at different speeds, the establishment of the thermal–mechanical model, identification of the thermal parameters, and prediction of the thermal-induced preload of bearings in the spindle. The speed-dependent thermal parameters were identified from thermal analysis through comparisons with transient temperature history, which were further used to model the thermal effects on the bearing preload and dynamic compliance of the milling spindle under different operating speeds. Current results of thermal–mechanical analysis also indicate that the internal temperature of the bearing can reach 40 °C, and the thermal elongation of the spindle tool is about 27 µm. At the steady state temperature of 15,000 rpm, the bearing preload is reduced by 40%, which yields a decrease in the bearing rigidity by approximately 16%. This, in turn, increases the dynamic compliance of the spindle tool by 22%. Comparisons of the experimental measurements and modeling data show that the variation in bearing preload substantially affects the modal frequency and stiffness of the spindle. These findings demonstrated that the proposed digital spindle model accurately mirrors real spindle characteristics, offering a foundation for monitoring performance changes and refining design, especially in bearing configuration and cooling systems. Full article

In this work, a modified numerical algorithm that couples the quasi-static theory with the mixed thermal elastohydrodynamic lubrication (mixed-TEHL) model is proposed to examine the mechanical properties and lubrication performance of the spindle bearing that is used in a high-speed machine tool with spinning. The non-Newtonian fluid characteristics of the lubricant and the non-Gaussian surface roughness are also considered. Moreover, the mechanical properties and lubrication state of the bearing are examined in various service environments. The results indicate that the temperature reduces the lubrication efficiency, which in turn exerts a significant impact on the mechanical properties. The lubrication that either behaves in the manner of Newtonian or non-Newtonian fluid has a relatively negligible influence on the bearing working state, while the non-Gaussian surface roughness significantly alters the oil film thickness and temperature. Calculations with different operating conditions demonstrate that the operating parameters (i.e., axial load, rotation speed) will directly affect the performance of the bearings via the changes in the oil film thickness and the temperature. Full article

Ceramic-copper substrates, as high-power, load-bearing components, are widely used in new energy vehicles, electric locomotives, high-energy lasers, integrated circuits, and other fields. The service length will depend on the substrate’s copper-coated surface quality, which frequently achieved by utilising an abrasive strip polishing procedure on the substrate’s copper-coated surface. Precision diamond fly-cutting processing machine tools were made because of the low processing accuracy and inability to match the production line’s efficiency. An analysis of the fly-cutting machining principle and the structural makeup of the ceramic-copper substrate is the first step in creating a roughness prediction model based on a tool tip trajectory. This model demonstrates that a shift in the tool tip trajectory due to spindle runout error directly impacts the machined surface’s roughness. The device’s structural optimisation design is derived from the above analyses and implemented using finite element software. Modal and harmonic response analysis validated the machine’s gantry symmetrical structural layout, a parametric variable optimisation design optimised the machine tool’s overall dimensions, and simulation validated the fly-cutterring’s constituent parts. Enhancing the machine tool’s stability and motion accuracy requires using the LK-G5000 laser sensor to measure the guideway’s straightness. The result verified the machine tool’s design index, with the Z- and Y-axes’ straightness being better than 2.42 $\mathsf{\mu }$m$/800$ mm and 2.32 $\mathsf{\mu }$m$/200$ mm, respectively. Ultimately, the device’s machining accuracy was confirmed. Experiments with flying-cut machining on a 190 × 140 mm ceramic-copper substrate yielded a roughness of Sa9.058 nm. According to the experimental results, the developed machine tool can fulfil the design specifications. Full article

This study addressed the issues related to the difficulty of determining the operating status of machine tool spindle bearings due to the high rotational speeds and rapid temperature fluctuations. This paper presents an optimized model that combines Convolutional Neural Networks (CNNs) and Informer to dynamically predict the temperature rise process of bearings. Taking the H7006C angular contact ball bearing as the research object, a combination of experimental data and simulations was used to obtain the training dataset. Next, a model for predicting the temperature rise of the bearing was constructed using CNN + Informer and the structural parameters were optimized. Finally, the model’s generalization ability was then verified by predicting the bearing temperature rise process under various working conditions. The results show that the error of the simulation data source model was less than 1 °C at steady state; the temperature error of the bearing temperature rise prediction model was less than 0.5 °C at both the temperature rise and steady-state stages under variable rotational speeds and variable load conditions compared to Informer and Long Short Term Memory (LSTM) models; the maximum prediction error of the operating conditions outside the dataset was less than 0.5 °C, and the temperature rise prediction model has a high accuracy, robustness, and generalization capability. Full article