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.

Thermal Error Modeling and Compensating of Motorized Spindle Based on Improved Neural Network

2010 ◽  
Vol 129-131 ◽  
pp. 556-560 ◽  
Author(s):  
Chun Li Lei ◽  
Zhi Yuan Rui
Keyword(s):  
Neural Network ◽  
High Speed ◽  
Thermal Deformation ◽  
Influence Factors ◽  
Thermal Error ◽  
Error Modeling ◽  
Motorized Spindle ◽  
Key Factor ◽  
Manufacturing Accuracy ◽  
Thermal Error Model

In a lot of factors, thermal deformation of motorized high-speed spindle is a key factor affecting the manufacturing accuracy of machine tool. In order to reduce the thermal errors, the reasons and influence factors are analyzed. A thermal error model, that considers the effect of thermodynamics and speed on the thermal deformation, is proposed by using genetic algorithm-based radial basis function neural network. The improved neural network has been trained and tested, then a thermal error compensation system based on this model is established to compensate thermal deformation. The experiment results show that there is a 79% decrease in motorized spindle errors and this model has high accuracy.

scholarly journals Thermal Error Prediction of Ball Screws Based on PSO-LSTM

2021 ◽  
Author(s):  
Xiangsheng Gao ◽  
Yueyang Guo ◽  
Dzonu Ambrose Hanson ◽  
Zhihao Liu ◽  
Min Wang ◽  
...  
Keyword(s):  
Neural Network ◽  
Time Series ◽  
High Speed ◽  
Thermal Characteristic ◽  
Thermal Error ◽  
Error Model ◽  
Cnc Machine Tools ◽  
Error Prediction ◽  
Model Based ◽  
Thermal Error Model

Abstract Thermal error of ball screws seriously affects the machining precision of CNC machine tools especially in high speed and precision machining. Compensation technology is one of the most effective methods to address the thermal issue, and the effect of compensation depends on the accuracy and robustness of the thermal error model. Traditional modeling approaches have major challenges in time-series thermal error prediction. In this paper, a novel thermal error model based on Long Short-Term Memory (LSTM) neural network and Particle Swarm Optimization (PSO) algorithm is proposed. A data-driven model based on LSTM neural network is established according to the time-series collected data. The hyperparameters of LSTM neural network are optimized by PSO and then a PSO-LSTM model is established to precisely predict the thermal error of ball screws. In order to verify the effectiveness and robustness of the proposed model, two thermal characteristic experiments based on step and random speed are conducted on a self-designed test bench. The results show that the PSO-LSTM model has higher accuracy compared with the RBF model and BP model with high robustness. The proposed method can be implemented to predict the thermal error of ball screws, and provide a foundation for thermal error compensation.

scholarly journals Optimal design of noise reduction and shape modification for traction gears of EMU based on improved BP neural network

2021 ◽  
Vol 69 (4) ◽  
pp. 373-388
Author(s):  
Zhaoping Tang ◽  
Min Wang ◽  
Xiaoying Xiong ◽  
Manyu Wang ◽  
Jianping Sun ◽  
...  
Keyword(s):  
Neural Network ◽  
Prediction Model ◽  
Bp Neural Network ◽  
High Speed ◽  
Transmission System ◽  
Gear Transmission ◽  
Noise Prediction ◽  
Vibration Acceleration ◽  
Acceleration Level ◽  
Gear Transmission System

Under high-speed operating conditions, the noise caused by the vibration of the traction gear transmission system of the Electric Multiple Units (EMU) will distinctly reduce the comfort of passengers. Therefore, analyzing the dynamic characteristics of traction gears and reducing noise from the root cause through comprehensive modification of gear pairs have become a hot research topic. Taking the G301 traction gear transmission system of the CRH380A high-speed EMU as the research object and then using Romax software to establish a parametric modification model of the gear transmission system, through dynamics, modal and Noise Vibration Harshness (NVH) simulation analysis, the law of howling noise of gear pair changes with modification parameters is studied. In the small sample training environment, the noise prediction model is constructed based on the priority weighted Back Propagation (BP) neural network of small noise samples. Taking the minimum noise of high-speed EMU traction gear transmission as the optimization goal, the simulated annealing (SA) algorithm is introduced to solve the model, and the optimal combination of modification parameters and noise data is obtained. The results show that the prediction accuracy of the prediction model is as high as 98.9%, and it can realize noise prediction under any combination of modification parameters. The optimal modification parameter combination obtained by solving the model through the SA algorithm is imported into the traction gear transmission system model. The vibration acceleration level obtained by the simulation is 89.647 dB, and the amplitude of the vibration acceleration level is reduced by 25%. It is verified that this modification optimization design can effectively reduce the gear transmission.

Comparison of Forecasting Methods for Thermal Error on High-Speed Motorized Spindle

2011 ◽  
Vol 291-294 ◽  
pp. 2991-2994 ◽  
Author(s):  
Chun Li Lei ◽  
Zhi Yuan Rui ◽  
Jun Liu ◽  
Jing Fang Fang
Keyword(s):  
Neural Network ◽  
Network Model ◽  
Neural Network Model ◽  
High Speed ◽  
Operation Time ◽  
Thermal Error ◽  
Motorized Spindle ◽  
Curve Fit ◽  
Forecast Precision ◽  
Nc Machine

In order to reduce the thermal error of the motorized spindle and improve the manufacturing accuracy of NC machine tool, the thermal error forecasting models based on multivariate autoregressive (MVAR) method and genetic radial basis function (GARBF) neural network method are proposed, respectively. According to different representations of generation mechanism of motorized spindle thermal deformation, operation efficiency and curve fit precision of these two models are compared. The studied results show that under the same temperature rise variable conditions, MVAR model and GARBF neural network model have almost the same convergence and operation time and relative errors of two models are less than 3%. The results also show that the MVAR model has higher forecast precision in the prediction former stages; in contrast, the GARBF neural network model has higher forecast precision in the latter stages.

Prediction for Percentage of Vehicle Entering Expressway Rest Area Based on BP Neural Network

2012 ◽  
Vol 178-181 ◽  
pp. 1956-1960
Author(s):  
Xiao Yan Shen ◽  
Hao Xue Liu ◽  
Jia Liu
Keyword(s):  
Neural Network ◽  
Prediction Model ◽  
Influencing Factors ◽  
High Precision ◽  
Bp Neural Network ◽  
High Speed ◽  
High Speed Railway ◽  
Neural Network Prediction ◽  
Network Prediction ◽  
Influence Degree

In order to scientifically decide the percentage of vehicle entering expressway rest area, based on analyzing the influencing factors relating to the percent of mainline traffic stopping, a BP neural network prediction model for it was put forward. Finally, The Xinzheng Rest Area (XRA) was taken as an example for verifying the feasibility of the prediction model and determining the influence degree of the Shijiazhuang-Wuhan high-speed railway on the percentage of mainline vehicles entering XRA. The result shows that the model had a high precision and reliability.

scholarly journals An Improved Prediction Model of IGBT Junction Temperature Based on Backpropagation Neural Network and Kalman Filter

Complexity ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Yu Dou
Keyword(s):  
Neural Network ◽  
Kalman Filter ◽  
Prediction Model ◽  
Bp Neural Network ◽  
High Speed ◽  
Rapid Development ◽  
Junction Temperature ◽  
Insulated Gate Bipolar Transistor ◽  
Network Estimation ◽  
The Stability

With the rapid development of emerging technologies such as electric vehicles and high-speed railways, the insulated gate bipolar transistor (IGBT) is becoming increasingly important as the core of the power electronic devices. Therefore, it is imperative to maintain the stability and reliability of IGBT under different circumstances. By predicting the junction temperature of IGBT, the operating condition and aging degree can be roughly evaluated. However, the current predicting approaches such as optical, physical, and electrical methods have various shortcomings. Hence, the backpropagation (BP) neural network can be applied to avoid the difficulties encountered by conventional approaches. In this article, an advanced prediction model is proposed to obtain accurate IGBT junction temperature. This method can be divided into three phases, BP neural network estimation, interpolation, and Kalman filter prediction. First, the validities of the BP neural network and Kalman filter are verified, respectively. Then, the performances of them are compared, and the superiority of the Kalman filter is proved. In the future, the application of neural networks or deep learning in power electronics will create more possibilities.

FEA of Thermal Characteristic of Motorized Spindle Based on ANSYS Workbench

2013 ◽  
Vol 437 ◽  
pp. 36-41
Author(s):  
Kai Kang Chen ◽  
Fu Ping Li ◽  
Yong Sheng Zhao
Keyword(s):  
High Speed ◽  
Thermal Deformation ◽  
Thermal Characteristic ◽  
Thermal Error ◽  
Machining Accuracy ◽  
Motorized Spindle ◽  
Thermal Boundary Conditions ◽  
Steady State Temperature ◽  
Transient Thermal ◽  
Ansys Workbench

Thermal deformation of high-speed motorized spindle has an important effect on improving the machining accuracy. In this paper the thermal boundary conditions of thermal deformation, including the heat generation of the motor and bearing and heat transfer coefficient, are calculated to simulate the steady-state temperature field distribution, transient thermal analysis and thermal deformation in ANSYS Workbench. They provide theoretically the data for the thermal error compensation of the spindle system.

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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