Research on Thermal Error Modeling of Motorized Spindle Based on BP Neural Network Optimized by Beetle Antennae Search Algorithm

High-speed motorized spindle heating will produce thermal error, which is an important factor affecting the machining accuracy of machine tools. The thermal error model of high-speed motorized spindles can compensate for thermal error and improve machining accuracy effectively. In order to confirm the high precision thermal error model, Beetle antennae search algorithm (BAS) is proposed to optimize the thermal error prediction model of motorized spindle based on BP neural network. Through the thermal characteristic experiment, the A02 motorized spindle is used as the research object to obtain the temperature and axial thermal drift data of the motorized spindle at different speeds. Using fuzzy clustering and grey relational analysis to screen temperature-sensitive points. Beetle antennae search algorithm (BAS) is used to optimize the weights and thresholds of the BP neural network. Finally, the BAS-BP thermal error prediction model is established. Compared with BP and GA-BP models, the results show that BAS-BP has higher prediction accuracy than BP and GA-BP models at different speeds. Therefore, the BAS-BP model is suitable for prediction and compensation of spindle thermal error.

Thermal Error Prediction of Ball Screws Based on PSO-LSTM

Thermal error of ball screws seriously affects the machining precision of CNC machine tools especially in high speed and precision machining. Compensation technology is one of the most effective methods to address the thermal issue, and the effect of compensation depends on the accuracy and robustness of the thermal error model. Traditional modeling approaches have major challenges in time-series thermal error prediction. In this paper, a novel thermal error model based on Long Short-Term Memory (LSTM) neural network and Particle Swarm Optimization (PSO) algorithm is proposed. A data-driven model based on LSTM neural network is established according to the time-series collected data. The hyperparameters of LSTM neural network are optimized by PSO and then a PSO-LSTM model is established to precisely predict the thermal error of ball screws. In order to verify the effectiveness and robustness of the proposed model, two thermal characteristic experiments based on step and random speed are conducted on a self-designed test bench. The results show that the PSO-LSTM model has higher accuracy compared with the RBF model and BP model with high robustness. The proposed method can be implemented to predict the thermal error of ball screws, and provide a foundation for thermal error compensation.

Thermal behavior analysis and experimental study on the vertical machining center spindle

Thermal errors caused by spindle rotation is a major factor that influences the precision stability of CNC machine tools. To determine an effective method for reducing thermal errors, a thermal experiment was carried out on the spindle of a vertical drilling center. The thermal deformation mechanism and thermal error variations of the spindle are presented. Based on the generation, convection, and conduction theory of heat, the thermal field model of a spindle system is derived. The relationship between the thermal field and the radial thermal error is established using a physically based method. Finally, the effect of the thermal error model proposed is verified by both a simulation and experiment. The results recorded on the two CNC machining centers indicate that the average fitting accuracy of the theoretical model is up to 94.1%, which validates the high accuracy and strong robustness of the presented model.

Thermal monitoring and thermal deformation prediction for spherical machine tool spindles

Machine tool operations and processing can cause temperature changes in various components because of internal and external thermal effects. Thermal deformations caused by thermal effect in machine tools can result in errors in processing size or shape and decrease processing precision. Thus, this paper focuses on the analysis of heating during machine tool spindle?s high speed operation, which is the heat source that causes component and structural deformation. In this paper, thermal monitoring was used to build a thermal error prediction model. Temperature change around the spindle was measured with a DS18B20, then multiple regression analysis was used to establish the relationship between thermal deformation quantity and temperature fields at specific points. Finally, finite element analysis was used to build the thermal error model. A solution for the correlation coefficient was obtained using the least squares method. The result of this study verified that finite element analysis can predict front bearing and rear bearing temperature rise, and is consistent with laboratory results. The error in thermal steady-state deformation prediction was less than 2 ?m. This information can be used by the controller to effectively compensate the processing and improve processing precision.

One-dimensional model for axial thermal error in a micro high-speed spindle

This article proposes a robust and accurate axial thermal error model for a micro high-speed spindle. With measured temperatures, an inverse method is applied to obtain the heat source and temperature field in the spindle for demonstrating that there exists a uniform temperature distribution along the axial direction within the motor range. Hence, a simple one-dimensional heat transfer model is established to acquire the temperature and resulting thermal errors using only one measured temperature on housing surface. Jumped displacement when the spindle starts and stops and the nonlinear deformation on the bearings are satisfactorily treated in the model. The results show that the estimated thermal errors agree with the measured data for both constant and various speeds. In addition, the results reveal that spindle speed significantly affects the maximum thermal error. A short processing time is an advantage of the proposed method. The model promises effective integration in machine tools for compensating thermal errors.

Study on Temperature Measurement Point Optimization and Thermal Error Modeling of NC Machine Tools

Background: In precision machining, thermal error is the main source of machine tool error. And thermal error compensation is an effective method to reduce thermal error. Objective: In order to improve the prediction accuracy and computational efficiency of thermal error model, a new optimization method used for the selection of temperature measurement point is proposed. Method: This method is based on stepwise regression. According to the results of partial-F statistic, new variable is selected one by one, unapparent variables are deleted, and optimization selection of temperature measurement point is fulfilled, thermal error model of the NC machine tool is presented. Result: The new modeling method was used on NC machine tool, which reduced the temperature point number from 24 to 5. Moreover, model residual was less than 5µm after compensation. Conclusion: The result shows that the new thermal error model has higher prediction accuracy and less temperature variables.