Research on Thermal Error Compensation Technology of Grinding Machine Based on Neural Networks

2007 ◽  
Vol 359-360 ◽  
pp. 569-573 ◽  
Author(s):  
Qian Jian Guo ◽  
Jian Guo Yang ◽  
Xiao Ni Qi
Keyword(s):  
Neural Networks ◽  
Error Compensation ◽  
Thermal Error ◽  
Error Modeling ◽  
Abrasive Machining ◽  
Error Sources ◽  
Hardware System ◽  
The Neural Networks ◽  
Grinding Machine ◽  
Thermal Error Compensation

In order to fulfill the thermal error modeling of precision abrasive machining, a neural networks model is presented through analyzing thermal error sources of the grinding machine, the structure and algorithm of the neural networks is expatiated then；And because of reasonable sample and systematical training, the accuracy of thermal error models is improved. The hardware system for thermal error compensation is proposed finally, and an experiment is accomplished on the grinding machine. The result shows thermal errors is reduced from 6μm to 1μm.

Real Time Thermal Error Modeling and Compensation of 5-Axis NC Grinding Machine Tool

2007 ◽  
Vol 359-360 ◽  
pp. 210-214 ◽  
Author(s):  
Xiu Shan Wang ◽  
Jian Guo Yang ◽  
Hao Wu ◽  
Jia Yu Yan
Keyword(s):  
Machine Tool ◽  
Real Time ◽  
Thermal Error ◽  
Error Modeling ◽  
Volumetric Error ◽  
Sensing System ◽  
Grinding Machine ◽  
Thermal Error Model ◽  
Thermal Error Compensation ◽  
Nc Grinding

The thermal error model of the 5-axis grinding machine tool was acquired by the homogeneous coordinate transformation, including 17 thermal error components. The thermal volumetric error real time compensation model was built by using the multiple regression analysis. The thermal error compensation control system and the temperature sensing system were developed and used as real-time compensation for the 5-axis grinding machine tool.

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 and Compensation of the Direct Driving Type A/C Axis CNC Milling Head

2012 ◽  
Vol 220-223 ◽  
pp. 333-338
Author(s):  
Peng Zheng ◽  
Ling Liu
Keyword(s):  
Error Compensation ◽  
Thermal Error ◽  
Type A ◽  
Small Error ◽  
Error Modeling ◽  
Cnc Milling ◽  
Processing Accuracy ◽  
Siemens 840D ◽  
The Relationship ◽  
Thermal Error Compensation

The thermal error of direct driving type A/C axis CNC milling head in the practical processing is analyzed. Based on the theory of homogeneous coordinate transformation and small error hypothesis, the transformation matrixes between the coordinate system of kinematic pairs and the relationship between errors and compensations are obtained. The thermal error compensation model and the thermal error compensation system which embedded into the Siemens 840D system are established, and play an important role in increasing the processing accuracy.

Application of Autoregressive Distributed Lag (ADL) Model to Thermal Error Modeling on NC Machine Tools

2011 ◽  
Vol 103 ◽  
pp. 9-14 ◽  
Author(s):  
En Ming Miao ◽  
Xin Wang ◽  
Ye Tai Fei ◽  
Yan Yan
Keyword(s):  
Error Compensation ◽  
Machine Tools ◽  
Thermal Error ◽  
Error Modeling ◽  
Thermal Error Modeling ◽  
Distributed Lag ◽  
Autoregressive Distributed Lag ◽  
Nc Machine ◽  
Nc Machine Tools ◽  
Thermal Error Compensation

Thermal error modeling method is an important field of thermal error compensation on NC machine tools, it is also a key for improving the machining accuracy of machine tools. The accuracy of the model directly affects the quality of thermal error compensation. On the basis of multiple linear regression (MLR) model, this paper proposes an autoregressive distributed lag (ADL) model of thermal error and establishes an accurate ADL model by stepwise regression analysis. The ADL model of thermal error is established with measured data, it proved the ADL model is available and has a high accuracy on predicting thermal error by comparing with MLR models.

Research on Volumetric Error Measurement, Modeling and Compensation for NC Machine Tools

2014 ◽  
Vol 513-517 ◽  
pp. 4202-4205
Author(s):  
Hong Xin Zhang ◽  
Qian Jian Guo
Keyword(s):  
Machine Tool ◽  
Error Compensation ◽  
Machine Tools ◽  
Thermal Error ◽  
Error Modeling ◽  
Cnc Machine Tools ◽  
Machining Accuracy ◽  
Volumetric Error ◽  
The Impact ◽  
Thermal Error Compensation

With the increasing requirements of the machining accuracy of CNC machine tools, the impact of thermal deformation is growing. Thermal error compensation technology can predict and compensate the thermal errors in real-time, and improve the machining accuracy of the machine tool. In this paper, the research objects of thermal error compensation is expanded to the volumetric error of the machine tool, the volumetric error modeling of a three-axis machine tool is fulfilled and a compensator is developed for the compensation experiment, which provides scientific basis for the improvement of the machining accuracy.

Synthesis Error Modeling and Thermal Error Compensation of Five-Axis Machining Center

2006 ◽  
Vol 532-533 ◽  
pp. 49-52 ◽  
Author(s):  
Xiu Shan Wang ◽  
Jian Guo Yang ◽  
Qian Jian Guo
Keyword(s):  
Real Time ◽  
Error Compensation ◽  
Thermal Error ◽  
Error Model ◽  
Error Modeling ◽  
Thermal Displacement ◽  
Collector System ◽  
Machining Center ◽  
Five Axis ◽  
Thermal Error Compensation

The synthesis error model of UCP710 five-axis machining center is divided into two parts: the position and orientation error models, and the article gets their models which are used as real-time compensation. One data collector system of thermal displacement and temperature is developed and used as real-time compensation for UCP710. The results of thermal error compensation have proved that the error model is correct and collector system works well.

Thermal error compensation based on genetic algorithm and artificial neural network of the shaft in the high-speed spindle system

2016 ◽  
Vol 231 (5) ◽  
pp. 753-767 ◽  
Author(s):  
Chi Ma ◽  
Liang Zhao ◽  
Xuesong Mei ◽  
Hu Shi ◽  
Jun Yang
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Neural Networks ◽  
Error Compensation ◽  
High Speed ◽  
Back Propagation ◽  
Thermal Error ◽  
Machining Accuracy ◽  
Spindle System ◽  
Thermal Error Compensation

To improve the accuracy, generality and convergence of thermal error compensation model based on traditional neural networks, a genetic algorithm was proposed to optimize the number of the nodes in the hidden layer, the weights and the thresholds of the traditional neural network by considering the shortcomings of the traditional neural networks which converged slowly and was easy to fall into local minima. Subsequently, the grey cluster grouping and statistical correlation analysis were proposed to group temperature variables and select thermal sensitive points. Then, the thermal error models of the high-speed spindle system were proposed based on the back propagation and genetic algorithm–back propagation neural networks with practical thermal error sample data. Moreover, thermal error compensation equations of three directions and compensation strategy were presented, considering thermal elongation and radial tilt angles. Finally, the real-time thermal error compensation was implemented on the jig borer’s high-speed spindle system. The results showed that genetic algorithm–back propagation models showed its effectiveness in quickly solving the global minimum searching problem with perfect convergence and robustness under different working conditions. In addition, the spindle thermal error compensation method based on the genetic algorithm–back propagation neural network can improve the jig borer’s machining accuracy effectively. The results of thermal error compensation showed that the axial accuracy was improved by 85% after error compensation, and the axial maximum error decreased from 39 to 3.6 µm. Moreover, the X/ Y-direction accuracy can reach up to 82% and 85%, respectively, which demonstrated the effectiveness of the proposed methodology of measuring, modeling and compensating.

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