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Govindhasamy, James J., McLoone, Sean F., Irwin, George W., 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 |
| Related URLs: | |
| URI: | https://mural.maynoothuniversity.ie/id/eprint/684 |
| 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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