Harris, K. orcid.org/0000-0002-2531-7026, Triantafyllopoulos, K., Stillman, E. et al. (1 more author) (2016) A Multivariate Control Chart for Autocorrelated Tool Wear Processes. Quality and Reliability Engineering International, 32 (6). pp. 2093-2106. ISSN 1099-1638
Abstract
Full automation of metal cutting processes has been a long held goal of the manufacturing industry. One key obstacle to achieving this ambition has been the inability to monitor completely the condition of the cutting tool in real time, as premature tool breakage and heavy tool wear can result in substantial costs through damage to the machinery and increasing the risk of non-conforming items that have to be scrapped or reworked. Instead, the condition of the tool has to be indirectly monitored using modern sensor technology that measures the acoustic emission, sound, spindle power and vibration of the tool during a cut. An on-line monitoring procedure for such data is proposed. Firstly, the standard deviation is extracted from each sensor signal to summarise the state of the tool after each cut. Secondly, a multivariate autoregressive state space model is specified for estimating the joint effects and cross-correlation of the sensor variables in Phase I. Then we apply a distribution-free monitoring scheme to the model residuals in Phase II, based on binomial type statistics. The proposed methodology is illustrated using a case study of titanium alloy milling (a machining process used in the manufacture of aircraft landing gears) from the Advanced Manufacturing Research Centre in Sheffield, UK, and is demonstrated to outperform alternative residual control charts in this application.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2016 The Authors Quality and Reliability Engineering International Published by John Wiley & Sons Ltd. This is an open access article under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits use, distribution and reproduction in any medium, provided the original work is properly cited. |
Keywords: | Statistical Process Control (SPC); sensor data; tool condition monitoring; nonparametric control charts; multivariate autoregressive state space models |
Dates: |
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Institution: | The University of Sheffield |
Academic Units: | The University of Sheffield > Faculty of Science (Sheffield) > School of Mathematics and Statistics (Sheffield) |
Funding Information: | Funder Grant number ENGINEERING AND PHYSICAL SCIENCE RESEARCH COUNCIL (EPSRC) EP/K031406/1 |
Depositing User: | Symplectic Sheffield |
Date Deposited: | 18 May 2016 09:57 |
Last Modified: | 11 Apr 2017 17:07 |
Published Version: | http://dx.doi.org/10.1002/qre.2032 |
Status: | Published |
Publisher: | Wiley |
Refereed: | Yes |
Identification Number: | 10.1002/qre.2032 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:99574 |
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