Self learning-empowered thermal error control method of precision machine tools based on digital twin

Ma, Chi; Gui, Hongquan; Liu, Jialan

doi:10.1007/s10845-021-01821-z

Cited by 37 publications

(8 citation statements)

References 55 publications

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“…This is because traditional models such as multiple linear regression models are difficult to deal with thermal error data with strong nonlinearity. Thermal error has nonlinear long-term memory behavior for historical data, 21 while traditional models such as multivariate linear regression model cannot self-learn and update thermal error data with time series characteristics. 22 Therefore, the thermal error prediction model established by simple multivariate linear regression will produce a large number of errors in the prediction process.…”

Section: Calculation Of Thermal Elongation Of Beamsmentioning

confidence: 99%

Thermal error compensation method of truss robot beam structure based on mechanism and data drive

Chen

Sha

et al. 2023

International Journal of Advanced Robotic Systems

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As the main supporting component of the truss robot, the thermal deformation of the beam often has a great influence on the overall thermal error of the truss robot due to its large span. In order to improve the thermal error prediction accuracy of long-span truss robot, a thermal error prediction method based on multiple linear regression and long short-term memory network is proposed based on mechanism and data drive. Firstly, the multiple linear regression model is used to predict the thermal error, and the prediction error data processing. Secondly, the long short-term memory network is established. In order to improve the performance of the long short-term memory network more effectively, an improved particle swarm optimization algorithm is proposed to optimize the hyper-parameters of the long short-term memory network. Finally, the improved particle swarm optimization–long short-term memory network is used to correct the prediction error of the multiple linear regression model. The experimental results show that the combined thermal error prediction model based on multiple linear regression and improved particle swarm optimization–long short-term memory algorithm has higher prediction accuracy than multiple linear regression model and long short-term memory network. The method has stable prediction accuracy and can provide a basis for thermal error compensation.

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Section: Calculation Of Thermal Elongation Of Beamsmentioning

confidence: 99%

Thermal error compensation method of truss robot beam structure based on mechanism and data drive

Chen

Sha

et al. 2023

International Journal of Advanced Robotic Systems

View full text Add to dashboard Cite

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“…Furthermore, several researchers have built hybrid thermal error models by combining different algorithms to take advantage of their respective features [ 17 , 18 , 19 , 20 , 21 ]. More recently, digital twin technology was adopted by [ 22 ] to solve the problem of thermal errors. The authors utilized the digital twin concept to propose a self-learning-empowered error control framework for the real-time thermal error prediction and control.…”

Section: Introductionmentioning

confidence: 99%

A Regularized Regression Thermal Error Modeling Method for CNC Machine Tools under Different Ambient Temperatures and Spindle Speeds

Wei

Zhou

et al. 2023

Sensors

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Establishing a mathematical model to predict and compensate for the thermal error of CNC machine tools is a commonly used approach. Most existing methods, especially those based on deep learning algorithms, have complicated models that need huge amounts of training data and lack interpretability. Therefore, this paper proposes a regularized regression algorithm for thermal error modeling, which has a simple structure that can be easily implemented in practice and has good interpretability. In addition, automatic temperature-sensitive variable selection is realized. Specifically, the least absolute regression method combined with two regularization techniques is used to establish the thermal error prediction model. The prediction effects are compared with state-of-the-art algorithms, including deep-learning-based algorithms. Comparison of the results shows that the proposed method has the best prediction accuracy and robustness. Finally, compensation experiments with the established model are conducted and prove the effectiveness of the proposed modeling method.

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“…Moreover, some scholars have employed recurrent neural network (RNN) containing time-series properties [18], to predict thermal error by fully considering the influence of thermal hysteresis effects [19,20]. However, given the problem of gradient disappearance or explosion during the backpropagation of RNN [21], researchers have widely used LSTM nerual network for thermal error prediction [22][23][24][25].…”

Section: Introductionmentioning

confidence: 99%

An Axial Attention-BiLSTM-based method for predicting the migration of CNC machine tool spindle thermal error under varying working conditions

Sun,

Liu,

Qiu

et al. 2023

Preprint

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Spindle thermal error is a major factor affecting the machining accuracy of machine tools. The time-consuming experiments required to model the thermal error of each machine tool spindle makes exhaustive studies difficult. Therefore, it is critically important to develop a transferable spindle thermal error prediction model that ensures robustness and accuracy, which provides theoretical guidance for the thermal error compensation and accuracy improvement of different machine tools. To achieve this, this paper proposes a migratory prediction method based on spatial-temporal axial attention bidirectional long short-term memory (Axial Attention-BiLSTM) network to predict the thermal error of computer numerical control (CNC) machine tool spindles under varying working conditions. By analyzing the mechanism of spindle thermal error generation, the spindle system is divided into multiple temperature regions, and the importance of each region is automatically determined by using the Spatial Attention mechanism. At the same time, considering the historical dependence of thermal error, BiLSTM is used to fuse the previous and following time series information and determine the weights of different time steps by Temporal Attention mechanism to strengthen the times series memory of thermal error prediction. An Axial Attention-BiLSTM model for thermal error prediction is built based on the spindle test bench. Compared with the BiLSTM and long short-term memory (LSTM) models, this model shows better and more stable prediction performance when migrated to various working conditions of horizontal CNC grinding machines.

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Self learning-empowered thermal error control method of precision machine tools based on digital twin

Cited by 37 publications

References 55 publications

Thermal error compensation method of truss robot beam structure based on mechanism and data drive

Thermal error compensation method of truss robot beam structure based on mechanism and data drive

A Regularized Regression Thermal Error Modeling Method for CNC Machine Tools under Different Ambient Temperatures and Spindle Speeds

An Axial Attention-BiLSTM-based method for predicting the migration of CNC machine tool spindle thermal error under varying working conditions

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