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The thermal error of the motorized spindle is an essential factor affecting the machining accuracy of high-speed numerically controlled machines. The establishment of a high-speed motorized spindle thermal error model for thermal error compensation can effectively improve the impact of thermal errors on the machining accuracy of the machine tool. This paper proposes a sparrow search algorithm to optimize the Elman neural network to predict thermal errors in motorized spindles. First is the simulation analysis on thermal characteristics of A02 high-speed motorized spindle. Based on the simulation results, the position of the temperature measuring points is arranged in the temperature and thermal error experiment of the motorized spindle. The temperature and thermal displacement data of high-speed motorized spindle at different rotational speeds were collected; secondly, the method of combining pedigree clustering and k-means clustering is used to perform cluster analysis on each temperature measurement point, and the grey correlation degree is used to determine the correlation between temperature measurement points and thermal error. Three temperature-sensitive points were screened from ten temperature measurement points, which reduced the collinearity between temperature measurement points and the number of independent variables of the model. Finally, the weights and thresholds of the Elman neural network are optimized by the sparrow search algorithm, and the thermal error prediction model of motorized spindle based on SSA-Elman neural network is established and compared with Elman neural network and Particle Swarm Optimized Elman Neural Network prediction model. The results show that the SSA-Elman neural network model has the highest prediction accuracy and exhibits good stability and generalization ability.

The thermal deformation of high-speed motorized spindle will affect its reliability, so fully considering its thermal characteristics is the premise of optimal design. In order to study the thermal characteristics of high-speed motorized spindles, a coupled model of thermal-flow-structure was established. Through experiment and simulation, the thermal characteristics of spiral cooling motorized spindle are studied, and the U-shaped cooled motorized spindle is designed and optimized. The simulation results show that when the diameter of the cooling channel is 7 mm, the temperature of the spiral cooling system is lower than that of the U-shaped cooling system, but the radial thermal deformation is greater than that of the U-shaped cooling system. As the increase of the channel diameter of U-shaped cooling system, the temperature and radial thermal deformation decrease. When the diameter is 10 mm, the temperature and radial thermal deformation are lower than the spiral cooling system. And as the flow rate increases, the temperature and radial thermal deformation gradually decrease, which provides a basis for a reasonable choice of water flow rate. The maximum error between experiment and simulation is 2°C, and the error is small, which verifies the accuracy and lays the foundation for future research.

The thermal behavior of the motorized spindle is a key issue that restricts the accuracy and efficiency of machining centers. Spindle thermal error modeling and compensation methods usually predict thermal errors based on temperature sensors on the spindle. However, the undetectable temperature region inside the spindle is an important source of thermal deformation, which leads to the lack of sufficient robustness of existing thermal error models. To improve the robustness of real-time prediction of spindle thermal error, an enhanced modeling method based on a mixture of the mechanism model and machine learning is proposed. First, the thermal network of the spindle is developed to predict the transient temperature field by determining the parameters through finite element (FE) simulation and thermal behavior experiments. Then, a data-fusion approach of the predicted temperature field and the measured data was established to enhance thermal error models by providing more thermal characteristic information inside the spindle to the machine learning process. Cross-validation shows that this method is universal to various types of machine learning modeling methods, including GRU, LSTM, LSSVM, BP, and MLR. Compared with the traditional method of using only basic sensors, this proposed method greatly improves the accuracy and robustness at the same hardware cost. The root mean square error (RMSE) decreased by 17–59%, and the fluctuation range decreased by 38–60%. Compared with the traditional method of attaching additional temperature sensors on the spindle, it saves 85.7% of the sensor cost and reduces the average RMSE by −0.27–27% and the fluctuation range by 10–40%. Finally, a digital twin system with a physical-edge-cloud layer structure is established for real-time prediction of spindle thermal behavior based on the best cost–benefit configuration, the enhanced LSTM model with basic sensors. It improves the accuracy and robustness of thermal error prediction results with lower sensor cost and can monitor the temperature changes inside the spindle in real-time, providing the possibility for potential industrial applications of intelligent spindles.

This paper presents the development and evaluation of neural network models using a small input–output dataset to predict the thermal behavior of a high-speed motorized spindles. Different neural multi-output regression models were developed and evaluated using Keras, one of the most popular deep learning frameworks at the moment. ANN was developed and evaluated considering the following: the influence of the topology (number of hidden layers and neurons within), the learning parameter, and validation techniques. The neural network was simulated using a dataset that was completely unknown to the network. The ANN model was used for analyzing the effect of working conditions on the thermal behavior of the motorized grinder spindle. The prediction accuracy of the ANN model for the spindle thermal behavior ranged from 95% to 98%. The results show that the ANN model with small datasets can accurately predict the temperature of the spindle under different working conditions. In addition, the analysis showed a very strong effect of type coolant on spindle unit temperature, particularly for intensive cooling with water.

The high-speed and high-precision motorized spindle is the future development trend of the CNC machine tool field, and has become the focus of research in the world. High-speed motorized spindles tend to develop in the direction of high precision, high speed, low energy consumption, high efficiency, and high reliability. We undertake a through, systematic review of the development history perspective of the research on precision bearing technology, dynamic balancing technology, thermal error measurement and compensation technology with regard to the key technologies of high-speed motorized spindles. On this basis, the current level of development of key technologies for high-speed motorized spindles is analyzed, and the objective advantages and disadvantages of existing technologies are summarized. Finally, the development tendency of high-speed motorized spindle technology is predicted and foreseen.

With the increase of spindle speed, heat generation becomes the crucial problem of high-speed motorized spindle. In order to obtain the actual thermal behavior of a motorized spindle, a digital twin system for thermal characteristics is developed in this paper. The mechanism of digital twin for thermal characteristics is to simulate the thermal behavior of a machine tool through mapping and correcting the thermal boundary conditions using the data acquisition system and correction models. The proposed digital twin system includes three modules which are the digital twin software, the data acquisition system, and the physical model with embedding sensors. The digital twin software is developed based on the Qt with the C + + programming language and the secondary development of ANSYS. Correction models for thermal boundaries are proposed to correct the heat generation and thermal contact resistance using the temperatures measured by the data acquisition system at thermal key points. To verify the prediction accuracy of the digital twin system, an experiment is carried out on a motorized spindle. The experimental results show that the prediction accuracy of the digital twin system is greater than 95%. It is of great significance to improve the accuracy of thermal characteristics simulation and thermal optimization.

High-speed motorized spindles play a vital role in manufacturing. In the process of high-speed rotation of the motorized spindle, the built-in motor and the high-speed running bearing will produce a lot of heat, and its heating will seriously affect the machining accuracy of the machine tool and the life of the spindle assembly. Therefore, the research on the thermal characteristics and cooling technology of the high-speed motorized spindle is an important measure to reduce the fire and explosion of the machine tool, improve the stability of the spindle and the safety of the machine tool, and is of great significance to ensure the stable operation of the high-speed motorized spindle. Based on the heat source, this paper puts forward two heating ways of high-speed motorized spindle, including bearing heating and motor heating, and probes into the thermal characteristic mechanism of high-speed motorized spindle and the modeling technology of thermal error compensation of high-speed motorized spindle in detail, and summarizes the bearing cooling technology, the principle, scope of application, advantages and disadvantages of motor cooling technology, and put forward corresponding improvement measures. And the future development of high-speed motorized spindle is prospected, and some new ideas are provided for the improvement of its technology.

The thermal characteristics of the motorized spindle significantly affect the machining accuracy and efficiency, and many thermal models have been developed to investigate the factors that affect the spindle thermal characteristics. However, the thermomechanical coupling of the bearings with constant pressure preload is rarely considered in the present works. Thus, this paper developed a transient temperature model of motorized spindle to study the influence of the radial thermal stress on the heat generation of the constant pressure preloaded bearings. In this research, an analytical thermal stress model was established first by simplifying the components of the bearings into a rotating ring geometry to calculate the thermal stress loaded on the bearings. Meanwhile, a transient temperature model of the motorized spindle was established based on the finite element method (FEM). Then, the analytical model was integrated into the spindle transient thermal model, so that the heat generated by bearings and the motorized spindle temperature can be revised constantly,  through the iterative calculation between these two models. Finally, verification experiments with different work conditions clarify that the proposed transient thermal characteristic model of the motorized spindle is valid, and the study shows that it is necessary to consider the bearing heat generation induced by the radial thermal stress when the spindle runs at a high speed.

Grinding is a prevalent metal processing method extensively employed in industrial production. However, the high temperatures generated during the grinding process can elevate the temperature of the grinding motorized spindle, subsequently impacting its performance and lifespan. Therefore, the design and research of the grinding motorized spindle cooling system become particularly important. In order to study the thermal characteristics of the grinding motorized spindle, this paper studies the two heating paths of the grinding motorized spindle based on the heat source, including the heat loss of the motor and the friction heat of the bearing, and establishes a fluid heat transfer model. Through cooling system design and thermal characteristic simulation analysis, the U-shaped cooling grinding motorized spindle is determined. The experiment found that the spindle temperature rise results are consistent with the numerical simulation results. Although the vibration amplitude will increase with the increase of the rotational speed, it will always remain within a stable range, which verifies the rationality of the cooling system design and provides ideas and methods for the design and optimization of the cooling system of the grinding motorized spindle in the future.

The rotor operating stiffness of high-speed motorized spindles (HSMSs) is key to machining accuracy. Because HSMSs are difficult to load due to their high speeds, a contact loading device was developed to test rotor operating stiffness. The dynamic support stiffness of the front/rear bearings (DSSB) is the main factor affecting the rotor operating stiffness. Two novel experimental schemes for measuring the DSSB are proposed: (1) indirect measurement—by analysing deformation displacements at two points on the external loading rod of the HSMS and (2) direct measurement—by eddy current sensors installed near the front/rear bearings. Based on the experimental device and two experimental schemes, the influences of working condition parameters on the DSSB were tested. The results show that the proposed experimental device and two experimental schemes can effectively and accurately measure rotor operating stiffness and DSSB at speeds of up to 30,000 rpm. However, because the tapered connection gap between the loading rod and rotor increases the measured deformation displacement, the DSSB measured by the indirect measurement scheme was relatively small. The DSSB decreases with speed and increases with radial force and working temperature. This study provides a new experimental basis for the quality inspection of finished HSMSs and the verification of theoretical bearing stiffness models.