Ward, R. orcid.org/0000-0002-6201-0285, Ozkirimli, O. and Jones, B. (2021) Increasing part geometric accuracy in high speed machining using cascade iterative learning control. In: Ozturk, E., Curtis, D. and Ghadbeigi, H., (eds.) Procedia CIRP. 9th CIRP Conference on High Performance Cutting, 24-26 May 2021, Online conference. Elsevier BV , pp. 298-301.
Abstract
High speed machining provides high productivity and low machining cycle times. Post machining, there can exist differences between desired and measured part geometry due to tool deflection induced from higher feedrates. Reducing the feedrate leads to an increase in machining time. Using predicted drive responses on a virtual CNC with an integrated surface location error model, this research is the first time Iterative Learning Control (ILC) has been applied to reduce part geometry errors from tool deflection. Validation machining trials demonstrated that the ILC scheme improved machining performance whilst maintaining machining times when compared to a baseline part program.
Metadata
Item Type: | Proceedings Paper |
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Authors/Creators: |
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Editors: |
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Copyright, Publisher and Additional Information: | © 2021 The Author(s). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/). |
Keywords: | Iterative Learning Control; Form Error Reduction; Milling |
Dates: |
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Institution: | The University of Sheffield |
Academic Units: | The University of Sheffield > Faculty of Engineering (Sheffield) > Department of Automatic Control and Systems Engineering (Sheffield) |
Depositing User: | Symplectic Sheffield |
Date Deposited: | 30 Mar 2022 08:11 |
Last Modified: | 30 Mar 2022 08:11 |
Status: | Published |
Publisher: | Elsevier BV |
Refereed: | Yes |
Identification Number: | 10.1016/j.procir.2020.10.006 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:185223 |