Robust Machine Tool Thermal Error Modeling Through Thermal Mode Concept

2008 ◽  
Vol 130 (6) ◽  
Author(s):  
Jie Zhu ◽  
Jun Ni ◽  
Albert J. Shih
Keyword(s):  
Machine Tool ◽  
Thermal Error ◽  
Error Modeling ◽  
Thermal Mode ◽  
Thermal Error Modeling ◽  
Thermal Errors ◽  
Underlying Mechanisms ◽  
Modeling Strategy ◽  
Thermal Error Compensation ◽  
Insight Into

Thermal errors are among the most significant contributors to machine tool errors. Successful reduction in thermal errors has been realized through thermal error compensation techniques in the past few decades. The effectiveness of thermal error models directly determines the compensation results. Most of the current thermal error modeling methods are empirical and highly rely on the collected data under specific working conditions, neglecting the insight into the underlying mechanisms that result in thermal deformations. In this paper, an innovative temperature sensor placement scheme and thermal error modeling strategy are proposed based on the thermal mode concept. The modeling procedures for both position independent and position dependent thermal errors are illustrated through numerical simulation and experiments. Satisfactory results have been achieved in terms of model accuracy and robustness.

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.

Dynamic Modeling for Machine Tool Thermal Error Compensation

2003 ◽  
Vol 125 (2) ◽  
pp. 245-254 ◽  
Author(s):  
Hong Yang ◽  
Jun Ni
Keyword(s):  
Machine Tool ◽  
Model Performance ◽  
Thermal Error ◽  
Error Model ◽  
Error Modeling ◽  
Thermal Error Modeling ◽  
Modeling Methodology ◽  
On Line ◽  
Thermal Error Model ◽  
Thermal Error Compensation

This paper proposes a new thermal error modeling methodology called the Dynamic Thermal Error Modeling which improves the accuracy and robustness of the machine tool thermal error model. The characteristics of the thermoelastic system are investigated from the dynamic system viewpoint. The pseudo-hysteresis effect is revealed to be the major factor causing poor robustness of the conventional static thermal error model. System identification theory is applied to build the dynamic thermal error model for machine tool thermal error on-line prediction. The modeling procedure for the linear Output Error (OE) model is illustrated using simulation work for both one-dimensional spindle and two-dimensional machine structure thermal deformations. Model performance evaluation through spindle experiments shows that the thermal error dynamic model has advantages over the conventional static model in terms of model accuracy and robustness.

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.

Application of a Bayesian Network to Thermal Error Modeling and Analysis for Machine Tool

2010 ◽  
Vol 455 ◽  
pp. 616-620
Author(s):  
X. Li ◽  
Q. Lei ◽  
Z.H. Li
Keyword(s):  
Machine Tool ◽  
Bayesian Network ◽  
Error Compensation ◽  
Process Condition ◽  
Thermal Error ◽  
Error Modeling ◽  
Machining Process ◽  
Cnc Machine ◽  
Thermal Error Modeling ◽  
Thermal Error Compensation

CNC machine tool dynamic thermal error compensation has always been a hot issue to improving precision. This dissertation proposes a method of machine tool thermal error modeling during processing, based on Bayesian network theory, by describing the correlation between the various factors of generated the heat error, through the sample data, analyzed and simplified the intrinsic correlation between these various factors, established the basic thermal error compensation model, and used the network’s good characteristic of self-studying, combining the result of update collection data, continually modify the model to reflect the machining process condition changes. Finally, the experimental results show the feasibility of Bayesian network model, it was a stronger application for achieving the thermal error compensation.

Numerical Analysis of Thermal Error for a 4-Axises Horizontal Machining Center

2017 ◽  
Vol 868 ◽  
pp. 64-68
Author(s):  
Yu Bin Huang ◽  
Wei Sun ◽  
Qing Chao Sun ◽  
Yue Ma ◽  
Hong Fu Wang
Keyword(s):  
Machine Tool ◽  
Working Condition ◽  
Prediction Method ◽  
Thermal Error ◽  
Error Modeling ◽  
Model Verification ◽  
Design Stage ◽  
Thermal Errors ◽  
Machining Center ◽  
Machining Errors

Thermal deformations of machine tool are among the most significant error source of machining errors. Most of current thermal error modeling researches is about 3-axies machine tool, highly reliant on collected date, which could not predict thermal errors in design stage. In This paper, in order to estimate the thermal error of a 4-axise horizontal machining center. A thermal error prediction method in machine tool design stage is proposed. Thermal errors in workspace in different working condition are illustrated through numerical simulation and volumetric error model. Verification experiments shows the outcomes of this prediction method are basically correct.

Thermal Error Modeling and Prediction of a Heavy Floor-Type Milling and Boring Machine Tool

2015 ◽  
Author(s):  
Fengchun Li ◽  
Tiemin Li ◽  
Haitong Wang
Keyword(s):  
Boundary Conditions ◽  
Machine Tool ◽  
Superposition Principle ◽  
Engineering Application ◽  
Thermal Error ◽  
Error Modeling ◽  
Simulation Data ◽  
Thermal Error Modeling ◽  
Fea Model ◽  
Modeling And Prediction

Thermal error modeling and prediction of a heavy floor-type milling and boring machine tool was studied in this paper. An FEA model and a thermal network of the machine tool’s ram was established. The influence of boundary conditions on thermal error was studied to find out the boundary conditions that needn’t to be calculated precisely, reducing the time cost of the work. Superposition principle of heat sources was used in the FEA to get the simulation data of thermal error and temperature. A model based on the simulation data was established to predict the thermal error during the work process. An experiment was performed to verify the accuracy of the model. The result shows that the model accuracy is 87%. The method in this paper is expected to be used in engineering application.

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