By Topic

An optimal estimation for neural network by using genetic algorithm for the prediction of thermal deformation in machine tools

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$31 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

5 Author(s)
Chuan-Wei Chang ; Dept. of Mech. Eng., Chung Yuan Christian Univ., Chung Li, Taiwan ; Yuan Kang ; Ming-Hui Chu ; Chih-Pin Chiang
more authors

Thermal deformations cause 40-70% error during the manufacturing process for the machine tools. In order to improve the accuracy of the machine tools, this study proposes a hybrid model, which predicts thermal deformation by combining an ARIMA and a feed-forward neural network (FNN) models. The genetic algorithm (GA) method is used to optimize this prediction model. The GA is used to search the optimal normalization coefficients, number of ARMA outputs and number of hidden neurons of FNN. It can reduce the network size and improve the propagation accuracy. In this study, comparisons between conventional FNN and the proposed hybrid model with or without using GA. The compared results show that the proposed hybrid model has better accuracy than the conventional FNN model and most accurate can be obtained by the proposed hybrid using GA. The predicted results, the hybrid model with GA can reduce the thermal deformation to 2 μm.

Published in:

Control and Automation, 2005. ICCA '05. International Conference on  (Volume:2 )

Date of Conference:

26-29 June 2005