Thermal error prediction of motorized spindle for five-axis machining center based on analytical modeling and BP neural network

2021 ◽  
Vol 35 (1) ◽  
pp. 281-292
Author(s):  
Yang Liu ◽  
Xiaofeng Wang ◽  
Xiaogang Zhu ◽  
Ying Zhai
Keyword(s):  
Neural Network ◽  
Bp Neural Network ◽  
Analytical Modeling ◽  
Thermal Error ◽  
Error Prediction ◽  
Motorized Spindle ◽  
Machining Center ◽  
Five Axis

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.

Synthesis Error Analysis of NC Manufacturing and Error Prediction Modelling Based on BP Neural Network Technology

2011 ◽  
Vol 314-316 ◽  
pp. 1254-1257
Author(s):  
Hao Fan ◽  
Hang Li ◽  
Dong Hong Si ◽  
Yu Jun Xue ◽  
Guo Feng Wang ◽  
...  
Keyword(s):  
Neural Network ◽  
Finite Element ◽  
Prediction Model ◽  
Cutting Force ◽  
Bp Neural Network ◽  
Error Prediction ◽  
Network Technology ◽  
Element Analysis ◽  
Machining Center ◽  
Neural Network Technology

The method was proposed by use of the finite element analysis software ABAQUS and the BP neural network technology to build a synthesis error prediction model of a machining-center. Firstly the finite element model of a vertical machining center CINCINNATI ARROW750 was created by use of ABAQUS ,and the cutting force induced error was analyzed which resulted from the deformation of the machining-center’s components that was caused by the cutting force ;Secondly the geometric error of the machining-center was measured by use of the laser interometer,and the sample of synthesis error was obtained. Finally the synthesis error prediction model was obtained by use BP neural network,and through the comparison of predicted value and actual value of 25 groups of samples, the feasibility of error prediction model was verified.

Thermal Error Measurement, Modeling and Compensation for Motorized Spindle and the Research on Compensation Effect Validation

2014 ◽  
Vol 889-890 ◽  
pp. 1003-1008 ◽  
Author(s):  
Yu Qing Fu ◽  
Wei Guo Gao ◽  
Jin Yu Yang ◽  
Qing Zhang ◽  
Da Wei Zhang
Keyword(s):  
Compensation Effect ◽  
Great Influence ◽  
Thermal Characteristics ◽  
Thermal Error ◽  
Shift Function ◽  
Linear Regression Method ◽  
Motorized Spindle ◽  
Verification Method ◽  
Machining Center ◽  
Thermal Error Model

The motorized spindle is a primate part of the machine tool and its thermal characteristics have great influence on the accuracy of the machine tools. The thermal errors of the spindle of a certain type of precision horizontal machining center were measured with PLC acquisition system. By multivariate linear regression method, the axial thermal error model was built. Online real-time error compensation was implemented by applying the FANUC 18i CNC system external machine coordinate system shift function. A verification method was proposed which include three steps: model validation, compensation validation and experimental machining verification. The accuracy of the model was 84.1%, 64.9% and 49.4% respectively. The quantitative analysis results showed the precision was effectively improved and the compensation method was reliable.

scholarly journals Thermal Characteristics Analysis for Motorized Spindle System of CNC Machining Center

2019 ◽  
Vol 257 ◽  
pp. 02003
Author(s):  
Xiaolei Deng ◽  
Xinghui Zhang ◽  
Mucheng Zhang ◽  
Yibo Zhou ◽  
Huan Lin ◽  
...  
Keyword(s):  
Thermal Characteristics ◽  
Thermal Error ◽  
Cnc Machining ◽  
Transfer Coefficients ◽  
Motorized Spindle ◽  
Element Analysis ◽  
Spindle System ◽  
Heat Transfer Coefficients ◽  
Machining Center ◽  
Characteristics Analysis

Based on the comprehensive analysis of the heat sources of the motorized spindle system, the thermal loads, including the heat generation of bearing friction and the electromagnetic loss of the built-in motor, are carried out for a machining center motorized spindle system. And then, the convective heat transfer coefficients of the whole spindle system are analyzed. The thermal characteristics of the motorized spindle system are calculated by finite element analysis. The steady state temperature field distribution of the motorized spindle is obtained. It provides some references for improving the thermal characteristics of the motorized spindle and reducing the difficulty of thermal error compensation.

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.

Thermal Error Modeling of Rotary Axis Based on Convolutional Neural Network

2021 ◽  
pp. 1-13
Author(s):  
Chengyang Wu ◽  
Sitong Xiang ◽  
Wansheng Xiang
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Thermal Error ◽  
Error Modeling ◽  
Data Sets ◽  
Thermal Imager ◽  
Thermal Images ◽  
Rotary Axis ◽  
Different Temperatures ◽  
Five Axis

Abstract Rotary axes are the key components for five-axis CNC machines, while their motions are dramatically influenced by thermal issues. To precisely model the thermal error of rotary axis, a convolutional neural network (CNN) model is developed. To form data sets for the CNN, a laser interferometer is used to measure the angular positioning error at different temperatures and a thermal imager is taken to obtain thermal images of the rotary axis. The measured thermal error is fitted to a sine curve so that training parameters are reduced. And the thermal pixel values of the initial thermal image are subtracted from all the thermal images to consider the incremental thermal effect, so the influence of the initial temperature is negligible. Finally, a deep CNN model with multiple output classifications is designed to complete the data training, verifying and testing. The experimental results show that the prediction accuracy for the parameters is higher than 90%, and the percentage reduction in error is higher than 80%.

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