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.

Feedback Control Based Active Cooling With Pre-estimated Reliability For Stabilizing The Thermal Error of a Precision Mechanical Spindle

Thermal error stability (STE) of the spindle determines the machining accuracy of a precision machine tool. Here we propose a thermal error feedback control based active cooling strategy for stabilizing the spindle thermal error in long-term. The strategy employs a cooling system as actuator and a thermal error regression model to output feedback. Structural temperature measurements are considerably interfered by the active cooling, so the regression models trained with experimental data might output inaccurate feedbacks in unseen work conditions. Such inaccurate feedbacks are the major cause for excessive fluctuations and failures of the thermal error control processes. Independence of the thermal data is analyzed, and a V-C (Vapnik-Chervonenkis) dimension based approach is presented to estimate the generalization error bound of the regression models. Then, the model which is most likely to give acceptable performance can be selected, the reliability of the feedbacks can be pre-estimated, and the risk of unsatisfactory control effect will be largely reduced. Experiments under different work conditions are conducted to verify the proposed strategy, the thermal error is stabilized to be within a range smaller than 1.637μm, and thermal equilibrium time is advanced by more than 78.3%.

Application of the digital model thermal errors of machine tools in automated production

The article presents a predicting method for a machine tool thermal error based on a nonlinear autoregressive neural network with an external input, as well as methods for smoothing experimental data obtained from measuring devices by approximation using polynomial regression and the gray systems theory. The development of accurate and robust thermal models is a critical step in achieving high productivity in thermal deformation reduction techniques on machine tools. Because thermal deformations of the machine structure caused by temperature increase often lead to thermal errors and reduce the accuracy of machining parts. The use of neural networks is a promising direction in solving forecasting problems. The authors propose a block diagram of a thermal process digital twin based on a neural network, which can be used in automated production. The results of the experiment carried out for the machine model 400V are obtained in the form of an assessment of approximation quality and accuracy of the forecasting model. The results show that the use of the proposed smoothing methods and a model for predicting a machine tool thermal error based on a neural network can improve the forecast accuracy.

Thermal error analysis of taurenEDM machine tool based on FCM fuzzy clustering and RBF neural network

A method that combines temperature field detection, adaptive FCM (Fuzzy c-means) clustering algorithm and RBF (Radial basis function network) neural network model is proposed. This method is used to analyze the thermal error of the spindle reference point of the taurenEDM (Electro-discharge machining) machine tool. The thermal imager is used to obtain the temperature field distribution of the machine tool while the machine tool simulates actual operating conditions. Based on this, the arrangement of temperature measurement points is determined, and the temperature data of the corresponding measurement points are got by temperature sensors. In actual engineering, too many temperature measurement points can cause problems such as too high cost, too much wiring. And normal processing can be affected. In order to establish that the thermal error prediction model of the machine tool spindle reference point can meet the actual engineering needs, the adaptive FCM clustering algorithm is used to optimize the temperature measurement points. While collecting the temperatures of the optimized temperature measurement points, the displacement sensors are used to detect the thermal deformation data in X, Y, Z directions of the spindle reference position. Based on the test data, the RBF neural network thermal errors prediction model of the machine tool spindle reference point is established. Then, the test results are used to verify the accuracy of the thermal errors analysis model. The research method in this paper provides a system solution for thermal error analysis of the taurenEDM machine tool. And this builds a foundation for real-time compensation of the machine tool’s thermal errors.