scholarly journals Correction to: Thermal error prediction of machine tool spindle using segment fusion LSSVM

2021 ◽  
Author(s):  
Feng Tan ◽  
Guofu Yin ◽  
Kai Zheng ◽  
Xin Wang
Keyword(s):  
Machine Tool ◽  
Thermal Error ◽  
Error Prediction ◽  
Machine Tool Spindle

scholarly journals A method of thermal error prediction modeling for CNC machine tool spindle system

2021 ◽  
Author(s):  
Wenjie Cao ◽  
Haolin Li ◽  
Qiang Li
Keyword(s):  
Finite Element ◽  
Temperature Field ◽  
Prediction Model ◽  
Machine Tool ◽  
Thermal Error ◽  
Error Prediction ◽  
Cnc Machine Tool ◽  
Cnc Machine ◽  
Spindle System ◽  
Machine Tool Spindle

Abstract In order to improve the machining accuracy of the thermal error prediction model of CNC machine tools, a new method for calculating the position of the measuring points optimal combination researched on linear correlation is proposed, according to the thermal-mechanical finite element analysis(FEA) model of spindle system established after analyzing the thermal characteristics of heat source temperature field of CNC machine tool spindle system. Based on the correlation analysis(CA) of the finite element model of heat source temperature field of CNC machine tool spindle system, combined with the concept of mutual information (MI), this method measures the information of the measurement point variables including the thermal error variables and uses principal component analysis (PCA) to eliminate the collinearity effect within measuring point variables. By using multilinear regression(MR), The thermal error prediction model(CAMI-PCAMR) is established. The accuracy of the prediction model is verified by comparing the actual measurement thermal error with the predicted thermal error through the experimental measurement and analysis of the thermal error of the CNC end grinder test machine tool system. That the axial prediction accuracy of this method can reach 1.099 \(\mu m\), and the prediction radial accuracy can reach 1.28 \(\mu m\) under the variable ambient condition, so as to provide parameters and theoretical guidance for embedding temperature sensors in the machine tool to compensate thermal error in the design stage. And the experimental results also show that the CAMI-PCAMR method is superior to the gray correlation and fuzzy clustering(FC-GCA) modeling method.

scholarly journals The bearing parallel misalignment running model–based machine tool spindle thermal error analysis

2018 ◽  
Vol 10 (6) ◽  
pp. 168781401877862
Author(s):  
Yanfang Dong ◽  
Zude Zhou ◽  
Lihai Chen
Keyword(s):  
Machine Tool ◽  
Significant Influence ◽  
Thermal Error ◽  
Simulation Method ◽  
Contact Angles ◽  
Empirical Methods ◽  
Spindle Bearing ◽  
Machine Tool Spindle ◽  
Contact Loads ◽  
Parallel Misalignment

As a key component of the machine tool spindle, bearing has critical influences on the spindle thermal error. In particular, the installation errors of bearing have considerable effects upon the spindle thermal error by altering the bearings’ internal contact angles, contact loads, and friction torques for different ball positions, but have yet to be fully elucidated. In this article, the influence of installation errors on the resulting spindle thermal error was evaluated using both empirical methods and simulation method, with the ultimate aim of reducing installation error. Deviations within the bearing support were used to simulate bearing parallel misalignment; bearing parallel misalignment running model was built, and an analysis and comparison of various conditions were used to determine the influence, showing that the parallel misalignment has significant influence on the spindle Z direction thermal error.

The Thermal Error Prediction of NC Machine Tool Based on LS-SVM and Grey Theory

2009 ◽  
Vol 16-19 ◽  
pp. 410-414 ◽  
Author(s):  
Chang Long Zhao ◽  
Yi Qiang Wang ◽  
Xue Song Guan
Keyword(s):  
Support Vector Machine ◽  
Correlation Analysis ◽  
Prediction Model ◽  
Machine Tool ◽  
Least Squares ◽  
Thermal Error ◽  
Support Vector ◽  
Error Prediction ◽  
Nc Machine Tool ◽  
Nc Machine

In this paper, a hybrid method of correlation analysis based on the gray theory and the least squares support vector machine is proposed to model the thermal error of spindle of NC machine tool and predict the thermal error. The gray correlation analysis is used to optimize the measuring points of spindle. The optimum measuring points and the measured thermal error of spindle are regarded as the data to be trained to build the thermal error prediction model based on the least squares support vector machine (LS-SVM). The results show that the thermal error prediction model based on LS-SVM of NC machine tool has advantages of high precision and good generalization performance. The prediction model can be used in real-time compensation of NC machine tool and can prove the process precision and reduce cost.

scholarly journals An Unsupervised Regularization and Dropout based Deep Neural Network and Its Application for Thermal Error Prediction

2020 ◽  
Vol 10 (8) ◽  
pp. 2870
Author(s):  
Yang Tian ◽  
Guangyuan Pan
Keyword(s):  
Neural Network ◽  
Prediction Model ◽  
Machine Tool ◽  
Deep Neural Network ◽  
Thermal Error ◽  
Error Modeling ◽  
Error Prediction ◽  
Learning Technology ◽  
Heavy Duty ◽  
Self Organizing

Due to the large size of the heavy duty machine tool-foundation systems, space temperature difference is high related to thermal error, which affects to system’s accuracy greatly. The recent highly focused deep learning technology could be an alternative in thermal error prediction. In this paper, a thermal prediction model based on a self-organizing deep neural network (DNN) is developed to facilitate accurate-based training for thermal error modeling of heavy-duty machine tool-foundation systems. The proposed model is improved in two ways. Firstly, a dropout self-organizing mechanism for unsupervised training is developed to prevent co-adaptation of the feature detectors. In addition, a regularization enhanced transfer function is proposed to further reduce the less important weights of the process and improve the network feature extraction capability and generalization ability. Furthermore, temperature sensors are used to acquire temperature data from the heavy-duty machine tool and concrete foundation. In this way, sample data of thermal error predictive model are repeatedly collected from the same locations at different times. Finally, accuracy of the thermal error prediction model was validated by thermal error experiments, thus laying the foundation for subsequent studies on thermal error compensation.

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