Abstract
Thermal error of ball screws seriously affects the machining precision of computerized numerical control (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 radial basis function (RBF) model and back propagation (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.
Highlights
Ball screws, an important transmission component which converts the rotary motion into the linear motion, have been widely applied in CNC machine tools owing to its high efficiency, precision and stiffness [1]
A thermal experiment with step speed of the motor for obtaining the data of temperature and deformation is carried out to validate the effectiveness of Particle Swarm Optimization algorithm (PSO)-Long Short-Term Memory (LSTM) model, and another thermal experiment with random speed is carried out to verify the robustness of the proposed model further
A novel data-driven model based on PSO-LSTM is proposed for predicting thermally induced error of ball screws, where the deep learning model combining with intelligent optimization algorithm is established based on experimental results
Summary
An important transmission component which converts the rotary motion into the linear motion, have been widely applied in CNC machine tools owing to its high efficiency, precision and stiffness [1]. Yang et al [9] proposed a thermal error model based on cerebellar model articulation controller (CMAC) neural network which can search for the nonlinear and interaction characteristics between the thermal errors and temperature field on the machine tools. The research on thermal errors prediction of machine tools by using LSTM network is rarely reported, especially in the field of ball screws. In order to establish an accurate mapping relationship with time-varying between temperature fields and thermal errors, Particle swarm optimization algorithm (PSO) is employed to optimize the hyperparameters of LSTM network for improving the performance of the model. A deep learning model based on PSO-LSTM to predict thermally induced error of ball screws according to temperature and deformation data measured by temperature sensor and eddy current displacement sensor, respectively, is proposed in this research. It is established and optimized by the Matlab deep learning toolbox and global optimization toolbox respectively
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