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.

Quality Prediction of Laser Cladding Based on Evolutionary Neural Network

2010 ◽  
Vol 44-47 ◽  
pp. 1012-1017
Author(s):  
Zhao Mei Xu ◽  
Hai Bing Wu ◽  
Zong Hai Hong
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Neural Networks ◽  
Laser Cladding ◽  
Back Propagation ◽  
Maximum Relative Error ◽  
Technical Parameters ◽  
Evolutionary Neural Network ◽  
Real Value ◽  
Accuracy Of Prediction

Artificial neural networks were introduced in the area of laser cladding forming. The prediction model of surface quality in laser cladding parts, including the width, depth of cladding layer and dilution rate, was proposed based on the improved learned arithmetic. The model combined the global optimization searching performance of the genetic algorithm and localization of the back propagation(BP) neural networks. Five technical parameters were selected to test the reliability of the mode. The simulation and experimental results show that the evolutionary neural network based on genetic algorithm can effectively overcome the problem of falling into local minimum point. This method can get higher accuracy of prediction. It improves the measurement precision with the maximum relative error 2.14% between the predicted content and the real value.

scholarly journals Research on Thermal Error Modeling of Motorized Spindle Based on BP Neural Network Optimized by Beetle Antennae Search Algorithm

Machines ◽  
2021 ◽  
Vol 9 (11) ◽  
pp. 286
Author(s):  
Zhaolong Li ◽  
Bo Zhu ◽  
Ye Dai ◽  
Wenming Zhu ◽  
Qinghai Wang ◽  
...  
Keyword(s):  
Neural Network ◽  
Prediction Model ◽  
Bp Neural Network ◽  
High Speed ◽  
Search Algorithm ◽  
Thermal Error ◽  
Error Prediction ◽  
Machining Accuracy ◽  
Motorized Spindle ◽  
Thermal Error Model

High-speed motorized spindle heating will produce thermal error, which is an important factor affecting the machining accuracy of machine tools. The thermal error model of high-speed motorized spindles can compensate for thermal error and improve machining accuracy effectively. In order to confirm the high precision thermal error model, Beetle antennae search algorithm (BAS) is proposed to optimize the thermal error prediction model of motorized spindle based on BP neural network. Through the thermal characteristic experiment, the A02 motorized spindle is used as the research object to obtain the temperature and axial thermal drift data of the motorized spindle at different speeds. Using fuzzy clustering and grey relational analysis to screen temperature-sensitive points. Beetle antennae search algorithm (BAS) is used to optimize the weights and thresholds of the BP neural network. Finally, the BAS-BP thermal error prediction model is established. Compared with BP and GA-BP models, the results show that BAS-BP has higher prediction accuracy than BP and GA-BP models at different speeds. Therefore, the BAS-BP model is suitable for prediction and compensation of spindle thermal error.

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.

Prediction of Machine Tool Thermal Error Compensation Based on SVMR and ARM11

2015 ◽  
Vol 740 ◽  
pp. 120-126
Author(s):  
Zhi Peng Zhang ◽  
Kang Liu ◽  
Feng Guo
Keyword(s):  
Neural Network ◽  
Support Vector Machine ◽  
Machine Tool ◽  
Bp Neural Network ◽  
Error Compensation ◽  
Thermal Error ◽  
Support Vector ◽  
Experiment Research ◽  
Further Development ◽  
Thermal Error Compensation

In order to improve the process precision of the machine tool, further development of SVMR was achieved by QT Creator. Support vector machine was applied to the ARM11 development board, SVMR model was online trained and real-time predicted the values of machine tool thermal error. Compared with the widely used BP neural network, this method has the characteristics of high compensation precision and strong generalization ability. Experiment research has proved that the stronger effectiveness and higher accuracy using this method.

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.

Research on Thermal Error Modeling of YK3610 Hobbing Machine

2012 ◽  
Vol 426 ◽  
pp. 293-296
Author(s):  
Qian Jian Guo ◽  
Jian Guo Yang
Keyword(s):  
Neural Network ◽  
Error Compensation ◽  
Back Propagation ◽  
Thermal Error ◽  
Back Propagation Neural Network ◽  
Error Modeling ◽  
Daily Production ◽  
Machine Error ◽  
Thermal Error Modeling ◽  
Thermal Error Model

Thermal error modeling. Neural network. Gear hobbing machine. Error compensation. Abstract. Four key thermal sources of YK3610 hobbing machine were selected in this paper, and a thermal error model based on the four temperature variables was proposed by using back propagation neural network. A thermal error compensation system was developed based on the proposed model, and which has been applied to the YK3610 hobbing machine in daily production. The result shows that the prediction accuracy of thermal deformation in the YK3610 hobbing machine has been improved.

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