Thermal error analysis of taurenEDM machine tool based on FCM fuzzy clustering and RBF neural network

2021 ◽  
pp. 1-12
Author(s):  
Jianyong Liu ◽  
Yanhua Cai ◽  
Qinjian Zhang ◽  
Haifeng Zhang ◽  
Hu He ◽  
...  
Keyword(s):  
Neural Network ◽  
Temperature Field ◽  
Prediction Model ◽  
Machine Tool ◽  
Temperature Measurement ◽  
Reference Point ◽  
Clustering Algorithm ◽  
Rbf Neural Network ◽  
Thermal Error ◽  
Thermal Errors

A method that combines temperature field detection, adaptive FCM (Fuzzy c-means) clustering algorithm and RBF (Radial basis function network) neural network model is proposed. This method is used to analyze the thermal error of the spindle reference point of the taurenEDM (Electro-discharge machining) machine tool. The thermal imager is used to obtain the temperature field distribution of the machine tool while the machine tool simulates actual operating conditions. Based on this, the arrangement of temperature measurement points is determined, and the temperature data of the corresponding measurement points are got by temperature sensors. In actual engineering, too many temperature measurement points can cause problems such as too high cost, too much wiring. And normal processing can be affected. In order to establish that the thermal error prediction model of the machine tool spindle reference point can meet the actual engineering needs, the adaptive FCM clustering algorithm is used to optimize the temperature measurement points. While collecting the temperatures of the optimized temperature measurement points, the displacement sensors are used to detect the thermal deformation data in X, Y, Z directions of the spindle reference position. Based on the test data, the RBF neural network thermal errors prediction model of the machine tool spindle reference point is established. Then, the test results are used to verify the accuracy of the thermal errors analysis model. The research method in this paper provides a system solution for thermal error analysis of the taurenEDM machine tool. And this builds a foundation for real-time compensation of the machine tool’s thermal errors.

Study on the Application of the Combined Prediction Modeling Method to Thermal Error Modeling on NC Machine Tools

2007 ◽  
Vol 329 ◽  
pp. 779-784 ◽  
Author(s):  
Y.X. Li ◽  
Jian Guo Yang ◽  
Yu Yao Li ◽  
H.T. Zhang ◽  
G. Turyagyenda
Keyword(s):  
Prediction Model ◽  
Machine Tool ◽  
Thermal Error ◽  
Error Modeling ◽  
Primary Data ◽  
Analysis Model ◽  
Grey System ◽  
Trend Prediction ◽  
Thermal Error Modeling ◽  
Thermal Errors

Due to the complexity of machine tool thermal errors affected by various factors, a new combining prediction model, based on the theory of gery system GM (1,1) model, is applied to the trend prediction of machine tool thermal errors. The degree of smoothness of primary data sequence is first improved by function transform method and sequentially grey system GM (1,1) model is established; second, time series analysis model is established by remnant sequence of GM (1,1) model to amend the precision of grey system GM (1,1) model. Thus, the precision of combining prediction model is further improved. Through the prediction study on thermal error modeling in a spot NC turning center, testing results showed that combining prediction model can highly improve machine tool’s prediction precision and make it more effective for real-time compensation of machine tool thermal error.

scholarly journals An Unsupervised Regularization and Dropout based Deep Neural Network and Its Application for Thermal Error Prediction

2020 ◽  
Vol 10 (8) ◽  
pp. 2870
Author(s):  
Yang Tian ◽  
Guangyuan Pan
Keyword(s):  
Neural Network ◽  
Prediction Model ◽  
Machine Tool ◽  
Deep Neural Network ◽  
Thermal Error ◽  
Error Modeling ◽  
Error Prediction ◽  
Learning Technology ◽  
Heavy Duty ◽  
Self Organizing

Due to the large size of the heavy duty machine tool-foundation systems, space temperature difference is high related to thermal error, which affects to system’s accuracy greatly. The recent highly focused deep learning technology could be an alternative in thermal error prediction. In this paper, a thermal prediction model based on a self-organizing deep neural network (DNN) is developed to facilitate accurate-based training for thermal error modeling of heavy-duty machine tool-foundation systems. The proposed model is improved in two ways. Firstly, a dropout self-organizing mechanism for unsupervised training is developed to prevent co-adaptation of the feature detectors. In addition, a regularization enhanced transfer function is proposed to further reduce the less important weights of the process and improve the network feature extraction capability and generalization ability. Furthermore, temperature sensors are used to acquire temperature data from the heavy-duty machine tool and concrete foundation. In this way, sample data of thermal error predictive model are repeatedly collected from the same locations at different times. Finally, accuracy of the thermal error prediction model was validated by thermal error experiments, thus laying the foundation for subsequent studies on thermal error compensation.

scholarly journals A method of thermal error prediction modeling for CNC machine tool spindle system

2021 ◽  
Author(s):  
Wenjie Cao ◽  
Haolin Li ◽  
Qiang Li
Keyword(s):  
Finite Element ◽  
Temperature Field ◽  
Prediction Model ◽  
Machine Tool ◽  
Thermal Error ◽  
Error Prediction ◽  
Cnc Machine Tool ◽  
Cnc Machine ◽  
Spindle System ◽  
Machine Tool Spindle

Abstract In order to improve the machining accuracy of the thermal error prediction model of CNC machine tools, a new method for calculating the position of the measuring points optimal combination researched on linear correlation is proposed, according to the thermal-mechanical finite element analysis(FEA) model of spindle system established after analyzing the thermal characteristics of heat source temperature field of CNC machine tool spindle system. Based on the correlation analysis(CA) of the finite element model of heat source temperature field of CNC machine tool spindle system, combined with the concept of mutual information (MI), this method measures the information of the measurement point variables including the thermal error variables and uses principal component analysis (PCA) to eliminate the collinearity effect within measuring point variables. By using multilinear regression(MR), The thermal error prediction model(CAMI-PCAMR) is established. The accuracy of the prediction model is verified by comparing the actual measurement thermal error with the predicted thermal error through the experimental measurement and analysis of the thermal error of the CNC end grinder test machine tool system. That the axial prediction accuracy of this method can reach 1.099 \(\mu m\), and the prediction radial accuracy can reach 1.28 \(\mu m\) under the variable ambient condition, so as to provide parameters and theoretical guidance for embedding temperature sensors in the machine tool to compensate thermal error in the design stage. And the experimental results also show that the CAMI-PCAMR method is superior to the gray correlation and fuzzy clustering(FC-GCA) modeling method.

scholarly journals Application of the digital model thermal errors of machine tools in automated production

2021 ◽  
Vol 2094 (4) ◽  
pp. 042022
Author(s):  
V V Pozevalkin ◽  
A N Polyakov
Keyword(s):  
Neural Network ◽  
Machine Tool ◽  
Machine Tools ◽  
Polynomial Regression ◽  
Block Diagram ◽  
Thermal Error ◽  
External Input ◽  
Machine Model ◽  
Automated Production ◽  
Thermal Errors

Abstract The article presents a predicting method for a machine tool thermal error based on a nonlinear autoregressive neural network with an external input, as well as methods for smoothing experimental data obtained from measuring devices by approximation using polynomial regression and the gray systems theory. The development of accurate and robust thermal models is a critical step in achieving high productivity in thermal deformation reduction techniques on machine tools. Because thermal deformations of the machine structure caused by temperature increase often lead to thermal errors and reduce the accuracy of machining parts. The use of neural networks is a promising direction in solving forecasting problems. The authors propose a block diagram of a thermal process digital twin based on a neural network, which can be used in automated production. The results of the experiment carried out for the machine model 400V are obtained in the form of an assessment of approximation quality and accuracy of the forecasting model. The results show that the use of the proposed smoothing methods and a model for predicting a machine tool thermal error based on a neural network can improve the forecast accuracy.

The Thermal Error Prediction of NC Machine Tool Based on LS-SVM and Grey Theory

2009 ◽  
Vol 16-19 ◽  
pp. 410-414 ◽  
Author(s):  
Chang Long Zhao ◽  
Yi Qiang Wang ◽  
Xue Song Guan
Keyword(s):  
Support Vector Machine ◽  
Correlation Analysis ◽  
Prediction Model ◽  
Machine Tool ◽  
Least Squares ◽  
Thermal Error ◽  
Support Vector ◽  
Error Prediction ◽  
Nc Machine Tool ◽  
Nc Machine

In this paper, a hybrid method of correlation analysis based on the gray theory and the least squares support vector machine is proposed to model the thermal error of spindle of NC machine tool and predict the thermal error. The gray correlation analysis is used to optimize the measuring points of spindle. The optimum measuring points and the measured thermal error of spindle are regarded as the data to be trained to build the thermal error prediction model based on the least squares support vector machine (LS-SVM). The results show that the thermal error prediction model based on LS-SVM of NC machine tool has advantages of high precision and good generalization performance. The prediction model can be used in real-time compensation of NC machine tool and can prove the process precision and reduce cost.

Prediction on China's Energy Demand Based on RBF Neural Network Model

2013 ◽  
Vol 805-806 ◽  
pp. 1421-1424
Author(s):  
Xue Feng ◽  
Wuyunbilige Bao ◽  
Ben Ha
Keyword(s):  
Neural Network ◽  
Prediction Model ◽  
Neural Network Model ◽  
Energy Demand ◽  
Rbf Neural Network ◽  
Bp Network ◽  
Factors Affecting ◽  
Rbf Network ◽  
Network Prediction ◽  
Main Factors

Choose factors which influence the energy demand by the method of path analysis, build radial basis function (RBF) neural network model to predict energy demand in China. The RBF neural network is trained with the actual data of the main factors affecting energy demand during 1989-2003 and energy demand during 1993-2007 as learning sample with a good fitting effect. After testing network with the actual data of the main factors affecting energy demand during 2004-2007 and energy demand during 2008-2011, higher prediction accuracy can be obtained. By comparison with the BP network, RBF network prediction model outperforms BP network prediction model, finally RBF network is applied to make prediction of energy consumption for the year 2013-2015.

The Research Based on RBF Neural Network in the Power of Prediction of Grain Depot

2012 ◽  
Vol 182-183 ◽  
pp. 1358-1361
Author(s):  
Le Xiao ◽  
Min Peng Hu
Keyword(s):  
Neural Network ◽  
Time Series ◽  
Prediction Model ◽  
Radial Basis Function ◽  
Basis Function ◽  
Rbf Neural Network ◽  
Electricity Consumption ◽  
Nonlinear Time Series ◽  
Modeling And Prediction ◽  
Radial Basis

According to the fact that the use of electricity in grain depot is nonlinear time series, the article introduces the prediction model of electricity based on Radial Basis Function Neural Network, and conducts the modeling and prediction by adopting the historical electricity consumption of a typical grain depot. As the result of simulation shows, the model obtains better forecasting results in grain depot electricity.

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