Thermal error prediction of ball screws based on PSO-LSTM

• Published in: International journal of advanced manufacturing technology Vol. 116; no. 5-6; pp. 1721 - 1735
• Main Authors: Gao, Xiangsheng, Guo, Yueyang, Hanson, Dzonu Ambrose, Liu, Zhihao, Wang, Min, Zan, Tao
• Format: Journal Article
• Language: English
• Published: London Springer London 01.09.2021; Springer Nature B.V
• Subjects: Algorithms; Back propagation; Back propagation networks; Ball screws; CAE) and Design; Computer-Aided Engineering (CAD; Data collection; Engineering; Error compensation; Industrial and Production Engineering; Machine tools; Mechanical Engineering; Media Management; Model accuracy; Neural networks; Numerical controls; Original Article; Particle swarm optimization; Precision machining; Radial basis function; Robustness (mathematics); Time series; Ball screw; Thermal error; PSO-LSTM; Data-driven; Modeling
• ISSN: 0268-3768; 1433-3015
• DOI: 10.1007/s00170-021-07560-y

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.