Application of ICA Method to Thermal Error Modeling of Gear Hobbing Machine

2007 ◽  
Vol 24-25 ◽  
pp. 309-314 ◽  
Author(s):  
Qian Jian Guo ◽  
Jian Guo Yang ◽  
Xiu Shan Wang
Keyword(s):  
Thermal Error ◽  
Error Modeling ◽  
Machining Accuracy ◽  
Single Chip ◽  
Single Chip Microcomputer ◽  
Thermal Error Modeling ◽  
Hardware System ◽  
Pitch Error ◽  
Gear Hobbing ◽  
Independent Component Analysis Method

Based on the synthetic analysis of thermal error sources, ICA (Independent component analysis) method is proposed to reduce the number of temperature sensor, and the selected temperature variables is used for thermal error modeling of gear hobbing machine. Finally, the hardware system of thermal error compensation is presented based on SCM (Single chip microcomputer) technique, and which is tested on Y3150K hobbing machine then. The results show that cumulative pitch error is reduced from 80μmto 20μm, and the machining accuracy is improved more than 2 grades.

Clustering Regression Modeling for Gear Hobbing Machine Thermal Error Compensation

2009 ◽  
Vol 416 ◽  
pp. 401-405
Author(s):  
Qian Jian Guo ◽  
Xiao Ni Qi
Keyword(s):  
Error Compensation ◽  
Thermal Error ◽  
Regression Modeling ◽  
Error Model ◽  
Error Modeling ◽  
Machining Accuracy ◽  
Thermal Error Modeling ◽  
Gear Hobbing ◽  
Thermal Error Model ◽  
Thermal Error Compensation

This paper proposes a new thermal error modeling methodology called Clustering Regression Thermal Error Modeling which not only improves the accuracy and robustness but also saves the time and cost of gear hobbing machine thermal error model. The major heat sources causing poor machining accuracy of gear hobbing machine are investigated. Clustering analysis method is applied to reduce the number of temperature sensors. Least squares regression modeling approach is used to build thermal error model for thermal error on-line prediction of gear hobbing machine. Model performance evaluation through thermal error compensation experiments shows that the new methodology has the advantage of higher accuracy and robustness.

Thermal error modeling of gear hobbing machine based on IGWO-GRNN

2020 ◽  
Vol 106 (11-12) ◽  
pp. 5001-5016 ◽  
Author(s):  
Zihui Liu ◽  
Bo Yang ◽  
Chi Ma ◽  
Shilong Wang ◽  
Yefeng Yang
Keyword(s):  
Thermal Error ◽  
Error Modeling ◽  
Thermal Error Modeling ◽  
Gear Hobbing

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.

scholarly journals Thermal error modeling based on BiLSTM deep learning for CNC machine tool

2021 ◽  
Author(s):  
Pu-Ling Liu ◽  
Zheng-Chun Du ◽  
Hui-Min Li ◽  
Ming Deng ◽  
Xiao-Bing Feng ◽  
...  
Keyword(s):  
Time Series ◽  
Deep Learning ◽  
Machine Tools ◽  
Thermal Error ◽  
Error Modeling ◽  
Numerical Control ◽  
Machining Accuracy ◽  
Depth Variation ◽  
Thermal Error Modeling ◽  
Proposed Model

AbstractThe machining accuracy of computer numerical control machine tools has always been a focus of the manufacturing industry. Among all errors, thermal error affects the machining accuracy considerably. Because of the significant impact of Industry 4.0 on machine tools, existing thermal error modeling methods have encountered unprecedented challenges in terms of model complexity and capability of dealing with a large number of time series data. A thermal error modeling method is proposed based on bidirectional long short-term memory (BiLSTM) deep learning, which has good learning ability and a strong capability to handle a large group of dynamic data. A four-layer model framework that includes BiLSTM, a feedforward neural network, and the max pooling is constructed. An elaborately designed algorithm is proposed for better and faster model training. The window length of the input sequence is selected based on the phase space reconstruction of the time series. The model prediction accuracy and model robustness were verified experimentally by three validation tests in which thermal errors predicted by the proposed model were compensated for real workpiece cutting. The average depth variation of the workpiece was reduced from approximately 50 µm to less than 2 µm after compensation. The reduction in maximum depth variation was more than 85%. The proposed model was proved to be feasible and effective for improving machining accuracy significantly.

scholarly journals Numerical and Experimental Investigation of the Temperature Rise of Cutting Tools Caused by the Tool Deflection Energy

Machines ◽  
2021 ◽  
Vol 9 (6) ◽  
pp. 122
Author(s):  
Yunxia Guo ◽  
Wenhua Ye ◽  
Xiang Xu
Keyword(s):  
Heat Source ◽  
Potential Energy ◽  
Temperature Rise ◽  
Cutting Tools ◽  
Thermal Error ◽  
Error Modeling ◽  
Flank Milling ◽  
Machining Accuracy ◽  
Tool Temperature ◽  
Thermal Error Modeling

Tool temperature variation in flank milling usually causes excessive tool wear, shortens tool life, and reduces machining accuracy. The heat source is the primary factor of the machine thermal error in the process of cutting components. Moreover, the accuracy of the thermal error modeling is greatly influenced by the formation mechanism of the heat source. However, the tool heat caused by the potential energy of the tool bending and twisting has essentially not been taken into consideration in previous research. In this paper, a new heat source that causes the thermal error of the cutting tools is proposed. The potential energy of the tools’ bending and twisting is calculated using experimental data, and how tool potential energy is transformed into heat via friction is explored based on the energy conservation. The temperature rise of the cutting tool is simulated by a lattice-centered finite volume method. To verify the model, the temperature separation of a tool edge is measured experimentally under the given cutting load. The results of the numerical analysis show that the rise in tool temperature caused by the tool’s potential energy is related to the time and position of the cutting edge involved in milling. For the same conditions, the predicted results are consistent with the experimental results. The proportion of temperature rise due to tool potential energy is up to 6.57% of the total tool temperature rise. The results obtained lay the foundation for accurate thermal error modeling, and also provide a theoretical basis for the force–thermal coupling process.

Thermal error modeling and compensation of long-travel nanopositioning stage

2012 ◽  
Vol 65 (1-4) ◽  
pp. 443-450 ◽  
Author(s):  
Jingshu Wang ◽  
Changan Zhu ◽  
Mingchi Feng ◽  
Wenqi Ren
Keyword(s):  
Thermal Error ◽  
Error Modeling ◽  
Thermal Error Modeling
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