scholarly journals Thermal Error Prediction and Compensation of YK3610 Hobbing Machine Based on BP Neural Networks

2015 ◽  
Vol 9 (1) ◽  
pp. 678-681 ◽  
Author(s):  
Qianjian Guo ◽  
Rufeng Xu ◽  
Xiaoni Qi
Keyword(s):  
Neural Networks ◽  
Ant Colony Algorithm ◽  
Back Propagation ◽  
Thermal Error ◽  
Test System ◽  
Minimum Problem ◽  
Bp Neural Networks ◽  
Thermal Errors ◽  
Gear Hobbing ◽  
Thermal Sources

In this study, error compensation technology was proposed to reduce thermal errors of a gear hobbing machine, and one experiment was carried out to verify the compensation effect. Different thermal sources were used as modeling variables, and a prediction model of thermal errors was presented based on back propagation (BP) neural networks. In order to solve local minimum problem of BP neural networks, ant colony algorithm was used for training its link weights. Finally, one test system was developed based on the presented model, and an experiment was fulfilled. The result shows that prediction performance of the model is very well, and the residual error is less than 5 μm after compensation.

Research on Thermal Error Modeling of NC Machine Tool Based on BP Neural Networks

2009 ◽  
Vol 626-627 ◽  
pp. 135-140 ◽  
Author(s):  
Qian Jian Guo ◽  
X.N. Qi
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Machine Tool ◽  
Ant Colony Algorithm ◽  
Thermal Error ◽  
Error Modeling ◽  
Minimum Problem ◽  
Bp Neural Networks ◽  
Nc Machine Tool ◽  
Nc Machine

Through analysis of the thermal errors affected NC machine tool, a new prediction model based on BP neural networks is presented, and ant colony algorithm is applied to train the weights of neural network model. Finally, thermal error compensation experiment is implemented, and the thermal error is reduced from 35μm to 6μm. The result shows that the local minimum problem of BP neural network is overcome, and the model accuracy is improved.

Improved Artificial BP Neural Networks and Genetic Algorithm Used to Predict the Purity of the Artificial Synthetic Hydrotalcite

2007 ◽  
Vol 280-283 ◽  
pp. 495-498
Author(s):  
Qiang Luo ◽  
Qing Li Ren
Keyword(s):  
Genetic Algorithm ◽  
Neural Networks ◽  
Prediction Model ◽  
Process Parameters ◽  
Learning Algorithm ◽  
Back Propagation ◽  
Raw Material ◽  
Algorithm Analysis ◽  
Neural Net ◽  
Bp Neural Networks

A prediction model for purity of the artificial synthetic hydrotalcite under varied process parameters based on improved artificial back-propagation (BP) neural networks is developed. And the non-linear relationship between the hydrotalcite purity and the raw material adding amount of NaOH, MgCl2 and AlCl3 was established based on BP learning algorithm analysis and convergence improvement. The hydrotalcite purity can be predicted by means of the trained neural net. Thus, by virtue of the prediction model, the future hydrotalcite purity can be evaluated under random complicated raw material amounts. Moreover, the best processing technology is optimized using the genetic algorithm.

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 BP Neural Networks in Damage Prediction of Multilayer Medium Penetration and Blasting

2012 ◽  
Vol 446-449 ◽  
pp. 1417-1420
Author(s):  
Xiao Ling Liu ◽  
Ting Lei ◽  
Yong Yao
Keyword(s):  
Experimental Data ◽  
Neural Networks ◽  
Learning Algorithm ◽  
Back Propagation ◽  
Bp Neural Networks ◽  
Bp Network ◽  
Damage Prediction ◽  
Multilayer Medium ◽  
Feed Forward Network ◽  
The Neural Networks

Back-propagation method (BP method) is the supervised learning algorithm that is the most widely and successfully used in feed forward network nowadays. This paper dealt with the penetration and blasting experimental data by BP Neural networks, including of the influence of the velocity and attack angles to damage of multilayer medium penetration and blasting. Through handling of the experimental data by the BP Network system, coupled effects of quantity of explosive and buried depth can be uncoupled. The curves of infundibular crater radius vs. quantity of explosive and infundibular crater depth vs. buried depth of explosive was given. Base on computing results, it is shown that the neural networks method can be used to predict the damage of multilayer medium penetration and blasting.

Thermal Error Modeling and Compensation for an INDEX-G200 Turning Center

2011 ◽  
Vol 215 ◽  
pp. 53-55
Author(s):  
Qian Jian Guo ◽  
Lei He ◽  
Guang Ming Zhu
Keyword(s):  
Prediction Accuracy ◽  
Back Propagation ◽  
Thermal Error ◽  
Error Modeling ◽  
Thermal Drift ◽  
Thermal Error Modeling ◽  
Thermal Errors ◽  
Whole Process ◽  
Turning Center ◽  
Bp Model

The purpose of this research is to fulfill thermal error modeling and compensation of an INDEX-G200 turning center. This paper presents the whole process of thermal error modeling and compensation by using back propagation neural networks. Results show that the BP model improves the prediction accuracy of thermal errors on the turning center and the thermal drift has been reduced from 39 to 11 after compensation.

Prediction of the Forest Health Based on BP Neural Networks

2013 ◽  
Vol 726-731 ◽  
pp. 4303-4306 ◽  
Author(s):  
Yong Wang ◽  
Zhuang Xiong
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Network Model ◽  
Neural Network Model ◽  
Bp Neural Network ◽  
Back Propagation ◽  
Forest Health ◽  
Bp Neural Networks ◽  
Forest Disease ◽  
Bp Neural Network Model

This paper simple introduced back propagation (BP) neural networks, and constructed a dynamic predict model, based on it to predict forest disease and insect and rat pest. Then it analyzed and simulated with the BP neural network model with the data produced in the recent ten years. The result indicated that the BP neural network model is reliable for predicting the forest disease and insect and rat pest. The method provides scientific foundation for the forestry management of studied area.

Study on Pilot Performance Model

2011 ◽  
Vol 383-390 ◽  
pp. 2545-2549
Author(s):  
Wei Liu ◽  
Cheng Kun Liu ◽  
Da Min Zhuang ◽  
Zhong Qi Liu ◽  
Xiu Gan Yuan
Keyword(s):  
Neural Networks ◽  
Eye Movement ◽  
Evaluation Method ◽  
Back Propagation ◽  
Performance Model ◽  
Experimental Comparison ◽  
Bp Neural Networks ◽  
Training Set ◽  
Pilot Performance ◽  
Movement Data

In order to evaluate pilot performance objectively, back propagation (BP) neural network model of 621423 form in topology with eye movement data was established. Data source of BP neural networks that came from former experiment and random interpolation was divided into training set and test set and normalized. Based on neural networks toolbox in Matlab, hidden layer nodes of BP networks were determined with empirical formula and experimental comparison ; BP algorithms in the toolbox were optimized; The training set data and test data were input into model for training and simulation; Pilot performance of the three skill levels was predicated and evaluated. The research shows that pilot performance can be accurately evaluated by setting up BP neural networks model with eye movement data and the evaluation method can provide a reference for flight training.

Optimizing BP Networks by Means of Genetic Algorithms in Quality of Laser Milling

2013 ◽  
Vol 690-693 ◽  
pp. 3338-3342
Author(s):  
Zhao Mei Xu ◽  
Zong Hai Hong ◽  
Gang Yang ◽  
Qing An Wang
Keyword(s):  
Genetic Algorithm ◽  
Neural Networks ◽  
Back Propagation ◽  
Maximum Relative Error ◽  
Bp Neural Networks ◽  
Technical Parameters ◽  
Evolutionary Neural Network ◽  
Laser Milling ◽  
Real Value ◽  
Accuracy Of Prediction

Artificial neural networks were introduced in the area of laser milling. The prediction model of surface quality in laser milling parts, including the width, depth of cladding layer, was proposed based on the back propagation (BP) neural networks. The model combined the global optimization searching performance of the genetic algorithm and the localization of the back propagation (BP) neural networks. Five technical parameters were selected to test the reliability of the model. 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. 21% between the predicted content and the real value.

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