Real-time thermal error prediction model for CNC lathes using a new one-dimension lumped capacity method

2021 ◽  
Author(s):  
Tie-jun Li ◽  
Chun-yu Zhao ◽  
Yi-min Zhang
Keyword(s):  
Prediction Model ◽  
Real Time ◽  
Thermal Error ◽  
One Dimension ◽  
Error Prediction ◽  
Capacity Method

Thermal Error Prediction of the Spindle Using Adaptive Neuro-Fuzzy Inference System

2015 ◽  
Vol 789-790 ◽  
pp. 263-267
Author(s):  
Yan Lei Li ◽  
Ming Yan Wang ◽  
You Min Hu ◽  
Bo Wu
Keyword(s):  
Prediction Model ◽  
Predictive Model ◽  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Thermal Error ◽  
Temperature Data ◽  
Current Sensor ◽  
Error Prediction ◽  
Inference System ◽  
Neuro Fuzzy

This paper proposes a new method to predict the spindle deformation based on temperature data. The method introduces ANFIS (adaptive neuro-fuzzy inference system). For building the predictive model, we first extract temperature data from sensors in the spindle, and then they are used as the inputs to train ANFIS. To evaluate the performance of the prediction, an experiment is implemented. Three Pt-100 thermal resistances is used to monitor the spindle temperature, and an inductive current sensor is used to obtain the spindle deformation. The experimental results display that our prediction model can better predict the spindle deformation and improve the performance of the spindle.

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.

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.

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.

A New Prediction Model for Thermally Induced Errors of a Turning Center

2014 ◽  
Vol 625 ◽  
pp. 411-416
Author(s):  
Hua Wei Chi ◽  
Yo Ren Lin
Keyword(s):  
Prediction Model ◽  
Real Time ◽  
Measurement System ◽  
Thermal Error ◽  
Cutting Conditions ◽  
Widespread Application ◽  
Thermally Induced ◽  
Line Measurement ◽  
Turning Center ◽  
On Line

Thermally induced errors and geometric errors are two main sources that affect the machine tool accuracy when machining. In the last decade, real time compensation method had received wide attention for its ability to reduce the thermal error cost–effectively. Although real-time thermal error compensation techniques have been successfully demonstrated in laboratories, several difficulties hinder its widespread application. The selection of temperature variables and the setup of the error measurement system are the most critical ones among these difficulties. In this paper, a new on line measurement system and a new model that predicts the thermal error of a turning center are developed. The on-line measurement system using a Renishaw’s LT02S probe system is capable of measuring thermal error of a CNC turning center in real cutting conditions. The neural network uses the cutting conditions as the mapping inputs to avoid problems occurred in the traditional temperature-error mapping model. Results show the proposed measurement system and prediction model can be used to accurately estimate the thermally induced error in real cutting conditions.

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 Study on Quality Prediction of 2219 Aluminum Alloy Friction Stir Welding Based on Real-Time Temperature Signal

Materials ◽  
2021 ◽  
Vol 14 (13) ◽  
pp. 3496
Author(s):  
Haijun Wang ◽  
Diqiu He ◽  
Mingjian Liao ◽  
Peng Liu ◽  
Ruilin Lai
Keyword(s):  
Aluminum Alloy ◽  
Friction Stir Welding ◽  
Prediction Model ◽  
Real Time ◽  
Support Vector ◽  
Weld Quality ◽  
Prediction Ability ◽  
Friction Stir ◽  
Temperature Signal ◽  
2219 Aluminum Alloy

The online prediction of friction stir welding quality is an important part of intelligent welding. In this paper, a new method for the online evaluation of weld quality is proposed, which takes the real-time temperature signal as the main research variable. We conducted a welding experiment with 2219 aluminum alloy of 6 mm thickness. The temperature signal is decomposed into components of different frequency bands by wavelet packet method and the energy of component signals is used as the characteristic parameter to evaluate the weld quality. A prediction model of weld performance based on least squares support vector machine and genetic algorithm was established. The experimental results showed that, when welding defects are caused by a sudden perturbation during welding, the amplitude of the temperature signal near the tool rotation frequency will change significantly. When improper process parameters are used, the frequency band component of the temperature signal in the range of 0~11 Hz increases significantly, and the statistical mean value of the temperature signal will also be different. The accuracy of the prediction model reached 90.6%, and the AUC value was 0.939, which reflects the good prediction ability of the model.

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