Identification of position of key thermal susceptible points for thermal error compensation of machine tool by neural network

1995 ◽  
Author(s):  
Dexian Zhang ◽  
Xiaolian Liu ◽  
Hanming Shi ◽  
Ri-Yao Chen
Keyword(s):  
Neural Network ◽  
Machine Tool ◽  
Error Compensation ◽  
Thermal Error ◽  
Thermal Error Compensation

Prediction of Machine Tool Thermal Error Compensation Based on SVMR and ARM11

2015 ◽  
Vol 740 ◽  
pp. 120-126
Author(s):  
Zhi Peng Zhang ◽  
Kang Liu ◽  
Feng Guo
Keyword(s):  
Neural Network ◽  
Support Vector Machine ◽  
Machine Tool ◽  
Bp Neural Network ◽  
Error Compensation ◽  
Thermal Error ◽  
Support Vector ◽  
Experiment Research ◽  
Further Development ◽  
Thermal Error Compensation

In order to improve the process precision of the machine tool, further development of SVMR was achieved by QT Creator. Support vector machine was applied to the ARM11 development board, SVMR model was online trained and real-time predicted the values of machine tool thermal error. Compared with the widely used BP neural network, this method has the characteristics of high compensation precision and strong generalization ability. Experiment research has proved that the stronger effectiveness and higher accuracy using this method.

Analysis on the Thermal Error Compensation Model of Direct-Drive A/C Bi-Rotary Milling Head

2011 ◽  
Vol 87 ◽  
pp. 59-62
Author(s):  
Peng Zheng ◽  
Xin Bao ◽  
Fang Cui
Keyword(s):  
Machine Tool ◽  
Error Compensation ◽  
Thermal Deformation ◽  
Thermal Error ◽  
Direct Drive ◽  
Cnc Machine ◽  
Compensation Model ◽  
Axis Rotation ◽  
Thermal Error Compensation ◽  
Deformation Error

The thermal deformation error that is the biggest error of effecting the machining precision of Direct-drive A/C Bi-rotary Milling Head was narrated in brief. Based on the introduce of the study status on the thermal error compensation techniques of CNC Machine tool, the momentum of thermal deformation of Bi-rotary Milling Head was analyzed. According to the Trigonometric Relations in A/C axis rotation angle of Bi-rotary Milling Head and the momentum of thermal deformation in Bi-rotary Milling Head and -axis respectively, a thermal error compensation model was established to make the Machine tool to compensate for thermal errors in -axis.

scholarly journals Thermal Error Compensation of Spindle System of Computer Numerically Controlled Machine Tools Through Experiments and Modeling

2020 ◽  
Vol 19 (4) ◽  
pp. 301-309
Author(s):  
Jianchen Wang ◽  
Tao Jiang ◽  
Junquan Shen ◽  
Junhao Dai ◽  
Zequan Pan ◽  
...  
Keyword(s):  
Machine Tool ◽  
Error Compensation ◽  
Machine Tools ◽  
Thermal Error ◽  
Local Optimum ◽  
Cnc Machine ◽  
Machining Error ◽  
Generalization Ability ◽  
Computer Numerically Controlled ◽  
Thermal Error Compensation

This paper attempts to solve the insufficient machining precision of computer numerically controlled (CNC) machine tools, which is induced by the thermal error of the spindle. Firstly, the relationship between machining error and thermal sensitive points was analyzed through experiments. On this basis, the backpropagation neural network (BPNN) was improved by particle swarm optimization (PSO). Next, the improved network (PSO-BPNN) was used to build a thermal error compensation (TCE) model for the spindle of machine tools. Taking VM-500T precision machine tool as the object, the temperature data were grouped through the optimization based on thermal imaging, grey relational analysis (GRA), and fuzzy clustering, to determine the temperature sensitive items that causes the thermal error. To speed up network convergence, the PSO algorithm was introduced to optimize the number of hidden layers and the number of hidden layer nodes of the BPNN, lifting the network from the local optimum trap. To enhance the generalization ability, the weights and thresholds of the BPNN were also improved by the PSO. After that, two TCE models were established for the spindle of the machine tool, respectively based on the original BPNN and PSO-BPNN. Contrastive experiments show that the PSO-BPNN TCE model achieved the better generalization ability, and improved the prediction accuracy of the machining error of the CNC machine tool.

The use of thermal imaging, temperature and distortion models for machine tool thermal error reduction

1998 ◽  
Vol 212 (8) ◽  
pp. 671-679 ◽  
Author(s):  
S R Postlethwaite ◽  
J P Allen ◽  
D G Ford
Keyword(s):  
Machine Tool ◽  
Thermal Imaging ◽  
Error Compensation ◽  
Rapid Assessment ◽  
Thermal Error ◽  
Indirect Measurement ◽  
Thermal Compensation ◽  
Machining Error ◽  
Compensation Technique ◽  
Thermal Error Compensation

Research has shown that up to 75 per cent of the total machining error can be produced by thermal distortion. Thermal error compensation can provide an attractive solution to this accuracy problem. Research has shown that thermal error compensation can reduce thermal errors to an acceptable level, but the techniques adopted have had inherent disadvantages that make them impractical for general use. This paper describes a novel indirect measurement based thermal error compensation technique that overcomes the main difficulties of applying thermal compensation, making it practical and generally applicable. The technique makes extensive use of thermal imaging for rapid assessment of machine tool thermal behaviour and off-line development of the compensation models. To illustrate the use of the technique it is applied to the head slide of a vertical machining centre.

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