A Multivariate Control Chart for Autocorrelated Tool Wear Processes

HARRIS, Keith, TRIANTAFYLLOPOULOS, Kostas, STILLMAN, Eleanor and MCLEAY, Thomas (2016). A Multivariate Control Chart for Autocorrelated Tool Wear Processes. Quality and Reliability Engineering International, 32 (6), 2093-2106.

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Official URL: https://onlinelibrary.wiley.com/doi/10.1002/qre.20...
Open Access URL: https://onlinelibrary.wiley.com/doi/epdf/10.1002/q... (Published version)
Link to published version:: https://doi.org/10.1002/qre.2032


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 online 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. © 2016 The Authors Quality and Reliability Engineering International Published by John Wiley & Sons Ltd.

Item Type: Article
Uncontrolled Keywords: Operations Research; 0102 Applied Mathematics; 0906 Electrical and Electronic Engineering
Identification Number: https://doi.org/10.1002/qre.2032
Page Range: 2093-2106
SWORD Depositor: Symplectic Elements
Depositing User: Symplectic Elements
Date Deposited: 13 Aug 2021 10:48
Last Modified: 13 Aug 2021 10:48
URI: https://shura.shu.ac.uk/id/eprint/28777

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