Study on optimal independent variables for the thermal error model of CNC machine tools

2018 ◽  
Vol 98 (1-4) ◽  
pp. 657-669 ◽  
Author(s):  
Xian Wei ◽  
Feng Gao ◽  
Yan Li ◽  
Dongya Zhang
Keyword(s):  
Machine Tools ◽  
Thermal Error ◽  
Error Model ◽  
Cnc Machine Tools ◽  
Cnc Machine ◽  
Independent Variables ◽  
Thermal Error Model

scholarly journals Identification, development and testing of thermal error compensation model for a headstock assembly of CNC turning centre

2014 ◽  
Vol 3 (2) ◽  
pp. 113 ◽  
Author(s):  
Ramesh Babu ◽  
V.Prabhu Raja ◽  
J. Kanchana ◽  
Devara Venkata Krishna
Keyword(s):  
Machine Tool ◽  
Analysis Of Variance ◽  
Error Compensation ◽  
Machine Tools ◽  
Thermal Error ◽  
Error Model ◽  
Transient Temperature ◽  
Cnc Machine ◽  
Grey Correlation ◽  
Thermal Error Model

In CNC machine tools, transient temperature variation in the headstock assembly is the major contributors for spindle thermal error. The compensation of thermal error is critical for ensuring the accuracy of machine tool. The performance of an error compensation system depends largely on the accuracy and robustness of the thermal error model. In the present work, a robust thermal error model is developed for minimizing the error in lateral direction of the spindle which significantly influences the geometrical accuracy of the workpiece. Analysis-of-variance (ANOVA) is applied to the results of the experiments in determining the percentage contribution of each individual temperature key point against a stated level of confidence. Based on the analysis of existing approaches for thermal error modeling of machine tools, an approach of LASSO (least absolute shrinkage and selection operator) is proposed in order to avoid the multi collinearity problem. The proposed method is an innovative variable selection method to remove redundant or unimportant temperature key points in the linear thermal error model and minimize the residual sum of squares. The predictive error model is found to have better robustness and accuracy in comparison to the combination of grey correlation and step wise linear regression for error compensation of CNC lathe. Keywords: Analysis Of Variance (ANOVA), CNC Machine Tool, Grey Correlation Analysis (GCA), Headstock Assembly, LASSO Regression, Mean Absolute Deviation (MAD), Mean Square Error (MSE), Robustness, Standard Deviation (SD), Thermal Error.

Application of Ubiquitous Sensor Network in Collection and Analysis of CNC Machine Tool Processing Status Information

2010 ◽  
Vol 455 ◽  
pp. 621-624
Author(s):  
X. Li ◽  
Y.Y. Yu
Keyword(s):  
Machine Tool ◽  
Sensor Network ◽  
Machine Tools ◽  
Thermal Error ◽  
Development Trend ◽  
Cnc Machine Tools ◽  
Cnc Machine Tool ◽  
Cnc Machine ◽  
Status Information ◽  
The Development Trend

Because of the practical requirement of real-time collection and analysis of CNC machine tool processing status information, we discuss the necessity and feasibility of applying ubiquitous sensor network(USN) in CNC machine tools by analyzing the characteristics of ubiquitous sensor network and the development trend of CNC machine tools, and application of machine tool thermal error compensation based on USN is presented.

Application of principal component algorithm in spindle thermal error modeling of CNC machine tools

2021 ◽  
Vol 29 (11) ◽  
pp. 2649-2660
Author(s):  
Xin-yuan WEI ◽  
◽  
Yu-chen CHEN ◽  
En-ming MIAO ◽  
Xu-gang FENG ◽  
...  
Keyword(s):  
Machine Tools ◽  
Thermal Error ◽  
Principal Component ◽  
Error Modeling ◽  
Cnc Machine Tools ◽  
Cnc Machine ◽  
Thermal Error Modeling

scholarly journals Thermal Error Prediction of Ball Screws Based on PSO-LSTM

2021 ◽  
Author(s):  
Xiangsheng Gao ◽  
Yueyang Guo ◽  
Dzonu Ambrose Hanson ◽  
Zhihao Liu ◽  
Min Wang ◽  
...  
Keyword(s):  
Neural Network ◽  
Time Series ◽  
High Speed ◽  
Thermal Characteristic ◽  
Thermal Error ◽  
Error Model ◽  
Cnc Machine Tools ◽  
Error Prediction ◽  
Model Based ◽  
Thermal Error Model

Abstract 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.

Sign in / Sign up

Export Citation Format

Share Document