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With the increasing importance of motorized spindles in high-end machining, the accuracy requirements for motorized spindles have been increasing. Therefore, the accuracy problem caused by thermal deformation of the motor spindle during machining has become a prominent and current topic of interest. The problem of thermal deformation of the spindle is due to the fact that a large amount of heat is accumulated inside the spindle during the high-speed rotation of the electric spindle, which has no timely temperature compensation measures and can only rely on the conditions of heat conduction, heat convection and heat radiation to transfer the heat to the outside world, which leads to the thermal expansion of the spindle. Aiming at this problem, this paper takes the A02 electric spindle as the research object, and collects and analyzes the temperature information of the current temperature rise of the key parts of the electric spindle and the thermal elongation of the spindle by constructing the experimental platform for thermal simulation and analysis; and then through the COMSOL steady-state simulation cloud diagram combined with the K-means clustering and gray correlation analysis, it filters out the four temperature points with the highest correlation degree of the temperature data, and constructs the POA-LSTM thermal error model to predict the thermal elongation of the spindle at different rotational speeds, and compared the accuracy of SSA-LSTM and LSTM thermal error models, at a high rotational speed of 10,000 r/min, the accuracies of Early LSTM, SSA-LSTM, and LSTM were 97.44 %, 90.27 %, and 86.66 %, respectively, and the model accuracy of POA-LSTM was about 98.257 %; thus the POA-LSTM thermal error model has high prediction accuracy and robustness.

Diabetic nephropathy (DN) is one of the main complications of diabetes. However, effective therapy to block or slow down the progression of DN is still lacking. San-Huang-Yi-Shen capsule (SHYS) has been shown to significantly improve renal function and delay the progression of DN. However, the mechanism of SHYS on DN is still unclear. In this study, we established a mouse model of DN. Then, we investigated the anti-ferroptotic effects of SHYS including the reduction of iron overload and the activation of cystine/GSH/GPX4 axis. Finally, we used a GPX4 inhibitor (RSL3) and ferroptosis inhibitor (ferrostatin-1) to determine whether SHYS ameliorates DN through inhibiting ferroptosis. The results showed that SHYS treatment was effective for mice with DN in terms of improving renal function, and reducing inflammation and oxidative stress. Besides, SHYS treatment reduced iron overload and upregulated the expression of cystine/GSH/GPX4 axis-related factors in kidney. Moreover, SHYS exhibited similar therapeutic effect on DN as ferrostatin-1, RSL3 could abolish the therapeutic and anti- ferroptotic effects of SHYS on DN. In conclusion, SHYS can be used to treat mice with DN. Furthermore, SHYS could inhibit ferroptosis in DN through reducing iron overload and upregulating the expression of cystine/GSH/GPX4 axis.

In high-precision machining machine tools, the high-speed electric spindle is the fundamental transmission component. Nonetheless, the electric spindle's small and tightly sealed internal structure may result in insufficient heat dissipation, resulting in significant heat accumulation during continuous operation. This might induce thermal displacement of the spindle and reduce machine tool machining precision. A response surface was established by a 2-factor 3-level interaction experiment utilizing Design-expert software to reduce the heat displacement of the electric spindle. The electric spindle cooling water jacket was optimized and front and rear bearing cooling water channels were added based on the response surface results. The thermal-solid coupling simulation was carried out using ANSYS, and the optimized data for the front and rear bearing temperature, as well as spindle thermal displacement, were obtained through simulation analysis. Comparing the simulation with the experimental data, it was found that the front and rear bearing temperature decreased by 4.34 °C and 8.97 °C, respectively, and the spindle thermal displacement was reduced by 16.57 μm, resulting in improved machining accuracy. The service life of the bearings was further prolonged by the decreased thermal stress between the inner and outer rings and the balls of the front and rear bearings.

The thermal error compensation process of high-speed motorized spindle needs to establish an effective prediction model. In this study, firstly, the thermal characteristics of a motorized spindle are tested, and the data at different speeds are obtained. Considering the disadvantage that the number of clusters in traditional clustering methods needs to be specified artificially, particle clustering and grey correlation analysis are selected to optimize the temperature measuring points. Aiming at the influence of data noise and the slow training speed, the depth limit learning machine is used to establish the thermal error prediction model. Considering that the output of deep extreme learning machine (DELM) is easily influenced by random input weights and biases, weighted mean of vectors algorithm (INFO) proposed in 2022 is chosen to optimize the two influencing factors. By establishing the INFO-DELM thermal error prediction model, and using the screened data to train the INDO-DELM model, the final prediction result is obtained. The results show that the accuracy of the INFO-DELM model is improved by 3%–26% compared with the basic DELM model. In addition, the INFO-DELM model shows excellent stability in both forward and backward forecasting, which proves that the model has strong adaptability. The optimization method of measuring points and INFO-DELM model in this paper provide a new idea for the field of thermal error compensation of motorized spindle.

In this article, to heighten the accuracy of thermal error forecast model of motorized spindle, a thermal error modeling method of kernel extreme learning machine based on snake optimization is advanced. ANSYS simulation software is used to simulate the thermal characteristics of the electric spindle, and the temperature field distribution is acquired. Then, according to the experimental requirements, a platform is built to obtain the axial temperature and thermal displacement data of the spindle. The FCM algorithm and grey relation analysis are utilized to optimize the survey points, and ten measuring points are reduced to four. The SO-KELM model is established based on the snake-optimized kernel extreme learning machine. Comparing the established model with the KELM model and PSO-KELM model, it is proved that SO-KELM model has good prediction accuracy and stability.

The thermal error of electric spindle is an important factor affecting the machining accuracy of machine tools. To establish the thermal error prediction model, the thermal characteristics of A02 electric spindle were simulated and analyzed by ANSYS, to determine the best temperature measuring point. The temperature and thermal error of A02 electric spindle at different rotational speeds were obtained by thermal characteristics experiment. Through the combination of systematic clustering and grey relational analysis, four best temperature measuring points were selected from ten temperature measuring points. A prediction model of least squares support vector machine (LSSVM) optimized by Aquila Optimizer (AO) is established and compared with the model optimized by Particle Swarm Optimization (PSO), it is superior to the latter in prediction accuracy (η), goodness of fit (R2), root mean square error (RMSE) and mean absolute error (MAE). The AO-LSSVM prediction model based on experimental data has a prediction accuracy of more than 94% for the thermal error of motorized spindle and has good stability and generalization ability.

High-speed electric spindle is an important part of computer numerical control (CNC) machining equipment, and the thermal displacement generated by the electric spindle during operation affects the electric spindle machining stability and machining accuracy. Error compensation for the high-speed motorized spindle can well compensate for the influence of thermal displacement on machine tool processing. Therefore, the prediction of thermal displacement of electric spindle is particularly important. In this paper, firstly, the temperature field information is obtained by simulating and analyzing the calculation of the heat generation and heat exchange inside the electric spindle. Set up the experimental platform with reasonable temperature measurement points according to the simulation results; secondly, the temperature sensitive points are screened by fuzzy C-mean clustering algorithm and Pearson correlation coefficient, which can effectively improve the covariance and correlation between temperature variables; finally, based on the screened data for thermal error modeling, an optimized extreme learning machine based on marine predator algorithm (MPA-ELM) is provided to predict the thermal displacement of electric spindles model. And comparing the model accuracy of extreme learning machine (ELM), MPA-ELM and extreme learning machine optimized by genetic algorithm (GA-ELM), the experimental data show that MPA-ELM has better prediction accuracy.