Govindhasamy, James J. and McLoone, Sean F. and Irwin, George W. and French, John J. and Doyle, Richard P. (2005) Neural modelling, control and optimisation of an industrial grinding process. Control Engineering Practice, 13 (10). pp. 1243-1258.

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Abstract

This paper describes the development of neural model-based control strategies for the optimisation of an industrial aluminium substrate disk grinding process. The grindstone removal rate varies considerably over a stone life and is a highly nonlinear function of process variables. Using historical grindstone performance data, a NARX-based neural network model is developed. This model is then used to implement a direct inverse controller and an internal model controller based on the process settings and previous removal rates. Preliminary plant investigations show that thickness defects can be reduced by 50% or more, compared to other schemes employed.

Item Type:	Article
Keywords:	Neural networks; Nonlinear modelling; NARX models; Disk grinding process; Multilayer perceptrons; Direct inverse model control; Internal model control
Academic Unit:	Faculty of Science and Engineering > Electronic Engineering
Item ID:	684
Depositing User:	Sean McLoone
Date Deposited:	23 Aug 2007
Journal or Publication Title:	Control Engineering Practice
Publisher:	Elsevier
Refereed:	Yes
URI:	http://www.elsevier.com/wps/find/journal...
Use Licence:	This item is available under a Creative Commons Attribution Non Commercial Share Alike Licence (CC BY-NC-SA). Details of this licence are available here

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