By Topic

RBF Neural Networks Process Model Based Optimization of Aluminum Powder Particle Size Distribution

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

2 Author(s)
Yonghui Zhang ; Coll. of Inf. Sci. & Technol., Hainan Univ. ; Cheng Shao

Nitrogen atomizing process is with nonlinearities, large time delay, strong coupling and severe uncertainty, and thus it is difficult to obtain the deterministic model by mechanistic method. In this paper, the process model based on RBF neural networks is presented to estimate the particle size distribution of aluminum powder by means of measurements of melted aluminum level and temperature, atomizing nitrogen temperature and pressure, and environment nitrogen temperature and pressure, and optimization of aluminum powder particle size distribution is implemented to improve the percentage of super-tiny aluminum powder. Comparisons of the aluminum powder particle size distribution before and after optimizing illustrate that the optimization of aluminum powder particle size distribution can improve the effect of nitrogen atomization and promote the percentage of super-tiny aluminum powder greatly

Published in:

Intelligent Control and Automation, 2006. WCICA 2006. The Sixth World Congress on  (Volume:2 )

Date of Conference:

0-0 0