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.
ASJC Scopus subject areas
- Control and Systems Engineering
- Industrial and Manufacturing Engineering
Govindhasamy, J. J., McLoone, S., Irwin, G., French, J. J., & Doyle, R. P. (2005). Neural modelling, control and optimisation of an industrial grinding process. Control Engineering Practice, 13(10), 1243-1258. https://doi.org/10.1016/j.congengprac.2004.11.006