Error Compensation on a NC-Machine Tool Based on Integrated Intelligent Computation

2011 ◽  
Vol 121-126 ◽  
pp. 1436-1442
Author(s):  
Huang Lin Zeng ◽  
Yong Sun ◽  
Xiao Hong Ren ◽  
Li Xin Liu
Keyword(s):  
Neural Network ◽  
Machine Tool ◽  
Error Compensation ◽  
Ant Colony Algorithm ◽  
Rough Set Theory ◽  
Dynamic Feedback ◽  
Standard Equipment ◽  
Nc Machine Tool ◽  
Nc Machine ◽  
Feedback Neural Network

This paper is a study of the application of integrated intelligent computation to solve the problems of error compensation for high-precision a NC machining system. The primary focus is on the development of integrated intelligent computation approach to get an error compensation system which is a dynamic feedback neural network embedded in a NC machine tool. Optimization of error measurement points of a NC machine tool is realized by way of application of error variable attribute reduction on rough set theory. A principal component analysis is used for data compression and feature extraction to reduce the input dimension of a dynamic feedback neural network and reduce training time of the network. Taking advantage of ant colony algorithm on training of a dynamic feedback neural network does the global search so that network can converge to get a global optimum. Positioning error caused in thermal deformation compensation capabilities were tested using industry standard equipment and procedures. The results obtained shows that this approach can effectively improve compensation precision and real time of error compensation on machine tools.

Research on Thermal Error Modeling of NC Machine Tool Based on BP Neural Networks

2009 ◽  
Vol 626-627 ◽  
pp. 135-140 ◽  
Author(s):  
Qian Jian Guo ◽  
X.N. Qi
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Machine Tool ◽  
Ant Colony Algorithm ◽  
Thermal Error ◽  
Error Modeling ◽  
Minimum Problem ◽  
Bp Neural Networks ◽  
Nc Machine Tool ◽  
Nc Machine

Through analysis of the thermal errors affected NC machine tool, a new prediction model based on BP neural networks is presented, and ant colony algorithm is applied to train the weights of neural network model. Finally, thermal error compensation experiment is implemented, and the thermal error is reduced from 35μm to 6μm. The result shows that the local minimum problem of BP neural network is overcome, and the model accuracy is improved.

Error Compensation of a NC Machining System Based on a Dynamic Feedback Neural Network

2011 ◽  
Vol 52-54 ◽  
pp. 1890-1894 ◽  
Author(s):  
Huang Lin Zeng ◽  
Yong Sun ◽  
Xiao Hong Ren ◽  
Li Xin Liu
Keyword(s):  
Neural Network ◽  
Error Compensation ◽  
Nc Machining ◽  
Machining Process ◽  
Dynamic Feedback ◽  
Machining System ◽  
Nc Machine ◽  
Feedback Neural Network ◽  
Set Up ◽  
Errors Analysis

Machining error of a NC machining system is a kind of comprehensive error in dynamically machining process; especially it is of errors with non-linear characteristics. In this paper, we will set up a kind of model of comprehensive errors analysis for a NC machining system and present an error compensation for high-precision a NC machining system by a dynamic feedback neural network embedded in a NC machine tool. The results obtained shows that this approach can effectively improve compensation precision and real time of error compensation on machine tools.

Reconstructing NC Program-Based Geometrical Error Compensation for Heavy-Duty NC Machine Tool

2011 ◽  
Vol 314-316 ◽  
pp. 2082-2086 ◽  
Author(s):  
Yong Lu ◽  
Jian Guang Li ◽  
Dong Gao ◽  
Feng Zhou
Keyword(s):  
Machine Tool ◽  
Error Compensation ◽  
Heavy Duty ◽  
Basic Algorithm ◽  
Nc Machine Tool ◽  
Compensation Algorithm ◽  
Movement Error ◽  
Nc Program ◽  
Nc Machine ◽  
Circular Interpolation

Heavy-duty NC machine tool is difficult and costly to evidently improve their precision via manufacturing technology only. It is proved being an effective approach to improve machine tool manufacturing precision based on software error compensation. In this paper, an error compensation algorithm based on reconstructing NC program is discussed. Following comprehensive discussion on basic algorithm of positioning error compensation in detail, linear interpolation and circular interpolation movement error compensation algorithm are further sketched in brief. To decrease the machining error, NC program is reconstructed before actual machining. The experiment results show that error compensation methods based on reconstructing NC program can improve profile accuracy of heavy-duty NC machine tools obviously.

scholarly journals Regular Neural Network Based Machining Parameters Optimization Model Of NC Machine Tool

2018 ◽  
Vol 232 ◽  
pp. 01006
Author(s):  
Sanping Wang ◽  
Junwen Chen ◽  
Wei Yan
Keyword(s):  
Neural Network ◽  
Energy Efficiency ◽  
Energy Consumption ◽  
Machine Tool ◽  
Process Parameters ◽  
Machine Tools ◽  
Machining Process ◽  
Machining Parameters ◽  
Nc Machine Tool ◽  
Nc Machine

Energy consumption process is the basis for energy efficiency improvement of machine tools. Most of the existing researches focus on the static modelling of energy consumption of a machine tool; however, there are a few studies that paid attention to that how process parameters influence the energy consumption of machine tools during processing. It is noted that the process parameters can be selected to reduce energy consumption during machining processes without additional investment. In this paper, a characteristic energy consumption model for NC machine tool was proposed. Then, the mapping rule between process parameters and energy consumption of machine tool was studied, and the model was solved with the regular neural network (RNN). Finally, the result was verified with an experiment of milling the surface of aluminium block, which can effectively improve the energy efficiency of machine tool. The experiment results are shown that regular neural network is used to optimize the process parameters and process the same machining characteristics; we analyze the in machining process of machine tool based on the three cutting parameters, and then, a model of energy consumption. We employ to learn, and use this trained model to select optimal parameters.

Geometric Error Compensation Methods of Numerical Control Machine Tool

2012 ◽  
Vol 426 ◽  
pp. 239-242
Author(s):  
Xiao Jun Wang ◽  
Xiao Guang Fu
Keyword(s):  
Machine Tool ◽  
Error Compensation ◽  
Reference Value ◽  
Geometric Error ◽  
Numerical Control ◽  
Control Machine Tool ◽  
Nc Machine Tool ◽  
Machining Center ◽  
Compensation Methods ◽  
Nc Machine

In this paper the characteristics of geometric errors is discussed in detail, error compensation methods used in productive practice and relevant examples are given. Finally, the application of error compensation in different situation is discussed according to the characteristics of machining center. The machine accuracy can be improved by error compensation. It has important practical reference value for reasonable use and maintaining of NC machine tool.

The Study of Compensation Method to Numerically-Controlled Machine Tool Geometrical Error

2014 ◽  
Vol 670-671 ◽  
pp. 1403-1405
Author(s):  
Lian Bing Wang
Keyword(s):  
Machine Tool ◽  
Error Compensation ◽  
Geometric Error ◽  
Compensation Method ◽  
System Error ◽  
Nc Machine Tool ◽  
Geometrical Error ◽  
Compensation Methods ◽  
Nc Machine ◽  
Software Upgrade

In this paper, the cause of nc machine tool geometric error made a more detailed analysis, the system error compensation methods are summarized, and on this basis this paper expounds the applications of all kinds of error compensation method, in order to further realize the accuracy of machine tool software upgrade to lay the foundation.

Application and Study of Fuzzy Neural Network Theory Based on Takagi-Sugeno Model to Thermal Error Modeling on NC Machine Tool

2013 ◽  
Vol 744 ◽  
pp. 147-152
Author(s):  
Zi Jian Liu ◽  
Zhi Min Yu ◽  
Si Ming Li ◽  
Yan Di Ai
Keyword(s):  
Neural Network ◽  
Machine Tool ◽  
Fuzzy Neural Network ◽  
Thermal Error ◽  
Error Modeling ◽  
Nc Machine Tool ◽  
Fuzzy Neural ◽  
Nc Machine ◽  
Sugeno Model ◽  
Takagi Sugeno

For the degree of thermal deformation nonlinear is high and difficult to predict, fuzzy neural network modeling (FNN) based on Takagi-Sugeno model was applied to the NC machine tool thermal error modeling thus the complete thermal error fuzzy neural network mathematical model on NC machine tool was established and network parameters initialization and learning method were discussed. Thermal error experiment was conducted on large NC gantry rail grinder spindle box system and two independent groups of spindle thermal error data were collected, one was used to establish thermal error fuzzy neural network prediction model and another one was used to verify the prediction accuracy of this model. The test results show that fuzzy neural network model has high prediction accuracy.

The Integration and Innovation Design of Single Chip Computer and NC Machine Tool Control

2014 ◽  
Vol 926-930 ◽  
pp. 478-481
Author(s):  
Jun Liang Liu ◽  
Zi Lun Li ◽  
Luo Cheng Li ◽  
Zi Jie Song
Keyword(s):  
Machine Tool ◽  
Error Compensation ◽  
Body System ◽  
Single Chip ◽  
Nc Machine Tool ◽  
Implementation Techniques ◽  
Single Chip Computer ◽  
Nc Machine ◽  
Compensation Device ◽  
Multi Body

Against to the problem of widely used of software error compensation, raises error compensation device to instead of PC to realize erro r compensation. And introduces multi-body system relating to error compensation, represents the feasibility and implementation techniques of software error compensation using SCM. This program will show great values in the field of CNC.

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