Neural modelling, control and optimisation of an industrial grinding process

J.J. Govindhasamy, Seán McLoone, George Irwin, J.J. French, R.P. Doyle

Research output: Contribution to journalArticlepeer-review

40 Citations (Scopus)


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. (c) 2004 Elsevier Ltd. All rights reserved.
Original languageEnglish
Pages (from-to)1243-1258
Number of pages16
JournalControl Engineering Practice
Issue number10
Publication statusPublished - Oct 2005

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Industrial and Manufacturing Engineering


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