scholarly journals Spindle Thermal Error Prediction Based on LSTM Deep Learning for a CNC Machine Tool

2021 ◽  
Vol 11 (12) ◽  
pp. 5444
Author(s):  
Yu-Chi Liu ◽  
Kun-Ying Li ◽  
Yao-Cheng Tsai
Keyword(s):  
Machine Tool ◽  
Short Term Memory ◽  
Estimation Method ◽  
Thermal Error ◽  
Machining Accuracy ◽  
Selection Algorithm ◽  
Cnc Machine ◽  
Point Selection ◽  
Thermal Displacement ◽  
Mean Square Errors

In the precision processing industry, maintaining the accuracy of machine tools for an extensive period is crucial. Machining accuracy is affected by numerous factors, among which spindle thermal elongation caused by an increase in machine temperature is the most common. This paper proposed a key temperature point selection algorithm and thermal error estimation method for spindle displacement in a machine tool. First, highly correlated temperature points were clustered into groups, and the characteristics of small differences within groups and large differences between groups were realized. The optimal number of key temperature points was then determined using the elbow method. Meanwhile, the long short-term memory (LSTM) modeling method was proposed to establish the relationship between the spindle thermal error and changes of the key temperature points. The results show the largest root mean square errors (RMSEs) of the proposed LSTM model and the key temperature point selection algorithm were within 0.6 µm in the spindle thermal displacement experiments with different temperature changes. The results demonstrated that the combined methodology can provide improved accuracy and robustness in predicting the spindle thermal displacement.

Thermal Error Modeling of a CNC Machine Tool

2013 ◽  
Vol 303-306 ◽  
pp. 1782-1785
Author(s):  
Chong Zhi Mao ◽  
Qian Jian Guo
Keyword(s):  
Machine Tool ◽  
Back Propagation ◽  
Thermal Error ◽  
Error Modeling ◽  
Machining Accuracy ◽  
Cnc Machine Tool ◽  
Cnc Machine ◽  
Thermal Error Modeling ◽  
Thermal Errors ◽  
Thermal Error Model

The purpose of this research is to improve the machining accuracy of a CNC machine tool through thermal error modeling and compensation. In this paper, a thermal error model based on back propagation networks (BPN) is presented, and the compensation is fulfilled. The results show that the BPN model improves the prediction accuracy of thermal errors on the CNC machine tool, and the thermal drift has been reduced from 15 to 5 after compensation.

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.

Sensitivity Analysis of Geometric Errors for Five-Axis CNC Machine Tool Based on Multi-Body System Theory

2012 ◽  
Vol 271-272 ◽  
pp. 493-497
Author(s):  
Wei Qing Wang ◽  
Huan Qin Wu
Keyword(s):  
System Theory ◽  
Sensitivity Analysis ◽  
Machine Tool ◽  
Geometric Error ◽  
Machining Accuracy ◽  
Geometric Errors ◽  
Body System ◽  
Cnc Machine ◽  
Multi Body ◽  
Five Axis

Abstract: In order to determine that the effect of geometric error to the machining accuracy is an important premise for the error compensation, a sensitivity analysis method of geometric error is presented based on multi-body system theory in this paper. An accuracy model of five-axis machine tool is established based on multi-body system theory, and with 37 geometric errors obtained through experimental verification, key error sources affecting the machining accuracy are finally identified by sensitivity analysis. The analysis result shows that the presented method can identify the important geometric errors having large influence on volumetric error of machine tool and is of help to improve the accuracy of machine tool economically.

Improvement of forecasting robustness of time series model for thermal error on CNC machine tool

2016 ◽  
Vol 24 (10) ◽  
pp. 2480-2489 ◽  
Author(s):  
苗恩铭 MIAO En-ming ◽  
刘 义 LIU Yi ◽  
董云飞 DONG Yun-fei ◽  
陈维康 CHEN Wei-kang
Keyword(s):  
Time Series ◽  
Machine Tool ◽  
Thermal Error ◽  
Time Series Model ◽  
Cnc Machine Tool ◽  
Cnc Machine

scholarly journals Effect of Supply Cooling Oil Temperature in Structural Cooling Channels on the Positioning Accuracy of Machine Tools

2019 ◽  
Vol 35 (6) ◽  
pp. 887-900 ◽  
Author(s):  
K.-Y. Li ◽  
W.-J. Luo ◽  
M.-H. Yang ◽  
X.-H. Hong ◽  
S.-J. Luo ◽  
...  
Keyword(s):  
Machine Tool ◽  
Thermal Deformation ◽  
Thermal Error ◽  
Machining Accuracy ◽  
Feed System ◽  
Positioning Accuracy ◽  
Cooling Channels ◽  
Position Accuracy ◽  
Machine Tool Accuracy ◽  
Volume Method

ABSTRACTIn this study, the thermal deformation of a machine tool structure due to the heat generated during operation was analyzed, and embedded cooling channels were applied to exchange the heat generated during the operation to achieve thermal error suppression. Then, the finite volume method was used to simulate the effect of cooling oil temperature on thermal deformation, and the effect of thermal suppression was experimentally studied using a feed system combined with a cooler to improve the positioning accuracy of the machine tool. In this study, the supply oil temperature in the structural cooling channels was found to significantly affect the position accuracy of the moving table and moving carrier. If the supply oil temperature in the cooling channels is consistent with the operational ambient temperature, the position accuracy of the moving table in the Y direction and the moving carrier in the X and Z directions has the best performance under different feed rates. From the thermal suppression experiments of the embedded cooling channels, the positioning accuracy of the feed system can be improved by approximately 25.5 % during the dynamic feeding process. Furthermore, when the hydrostatic guideway is cooled and dynamic feeding is conducted, positioning accuracy can be improved by up to 47.8 %. The machining accuracy can be improved by approximately 60 % on average by using the embedded cooling channels in this study. Therefore, thermal suppression by the cooling channels in this study can not only effectively improve the positioning accuracy but also enhance machining accuracy, proving that the method is effective for enhancing machine tool accuracy.

Analysis on the Thermal Error Compensation Model of Direct-Drive A/C Bi-Rotary Milling Head

2011 ◽  
Vol 87 ◽  
pp. 59-62
Author(s):  
Peng Zheng ◽  
Xin Bao ◽  
Fang Cui
Keyword(s):  
Machine Tool ◽  
Error Compensation ◽  
Thermal Deformation ◽  
Thermal Error ◽  
Direct Drive ◽  
Cnc Machine ◽  
Compensation Model ◽  
Axis Rotation ◽  
Thermal Error Compensation ◽  
Deformation Error

The thermal deformation error that is the biggest error of effecting the machining precision of Direct-drive A/C Bi-rotary Milling Head was narrated in brief. Based on the introduce of the study status on the thermal error compensation techniques of CNC Machine tool, the momentum of thermal deformation of Bi-rotary Milling Head was analyzed. According to the Trigonometric Relations in A/C axis rotation angle of Bi-rotary Milling Head and the momentum of thermal deformation in Bi-rotary Milling Head and -axis respectively, a thermal error compensation model was established to make the Machine tool to compensate for thermal errors in -axis.

scholarly journals Thermal Error Compensation of Spindle System of Computer Numerically Controlled Machine Tools Through Experiments and Modeling

2020 ◽  
Vol 19 (4) ◽  
pp. 301-309
Author(s):  
Jianchen Wang ◽  
Tao Jiang ◽  
Junquan Shen ◽  
Junhao Dai ◽  
Zequan Pan ◽  
...  
Keyword(s):  
Machine Tool ◽  
Error Compensation ◽  
Machine Tools ◽  
Thermal Error ◽  
Local Optimum ◽  
Cnc Machine ◽  
Machining Error ◽  
Generalization Ability ◽  
Computer Numerically Controlled ◽  
Thermal Error Compensation

This paper attempts to solve the insufficient machining precision of computer numerically controlled (CNC) machine tools, which is induced by the thermal error of the spindle. Firstly, the relationship between machining error and thermal sensitive points was analyzed through experiments. On this basis, the backpropagation neural network (BPNN) was improved by particle swarm optimization (PSO). Next, the improved network (PSO-BPNN) was used to build a thermal error compensation (TCE) model for the spindle of machine tools. Taking VM-500T precision machine tool as the object, the temperature data were grouped through the optimization based on thermal imaging, grey relational analysis (GRA), and fuzzy clustering, to determine the temperature sensitive items that causes the thermal error. To speed up network convergence, the PSO algorithm was introduced to optimize the number of hidden layers and the number of hidden layer nodes of the BPNN, lifting the network from the local optimum trap. To enhance the generalization ability, the weights and thresholds of the BPNN were also improved by the PSO. After that, two TCE models were established for the spindle of the machine tool, respectively based on the original BPNN and PSO-BPNN. Contrastive experiments show that the PSO-BPNN TCE model achieved the better generalization ability, and improved the prediction accuracy of the machining error of the CNC machine tool.

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