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
