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A cyber physical system for tool condition monitoring using electrical power and a mechanistic model

journal contribution
posted on 04.06.2020, 08:36 by Paul GoodallPaul Goodall, Dimitrios Pantazis, Andrew WestAndrew West
© 2020 Tool Condition Monitoring (TCM) systems, which aim to identify tool wear automatically to avoid damage to the machined part, are often not suitable for industrial applications due to: (i) sensing methods which can be expensive and invasive to install and (ii) testing regimes that only evaluate a narrow operating window under controlled conditions. To combat these issues, the research outlined in this paper explores the feasibility of TCM using electrical power consumption of an industrial Computer Numeric Control (CNC) cutting machine in combination with a mechanistic model for end milling operations. End milling of aluminium 6082 was performed over a range of cutting parameters (i.e. spindle speed, feed rate, tool diameter) and repeated with increasing levels of tool wear to ascertain the suitability of this approach. Reasonable correlation between the predicted and observed tool wear was found using the mechanistic model (R2 = 0.801), however variance between the cutting parameters highlights the limitations of the predictions. To combat this variance, an averaging window is taken over the course of a cutting program to reduce the overall error. A concept TCM system is then proposed, utilising cyber-physical models of the milling process to determine the width and depth of cuts for complex geometries which change in real time, with the challenges of implementing such a system discussed.

Funding

Adaptive Informatics for Intelligent Manufacturing (AI2M) : EP/K014137/1

Future Connected Smart Manufacturing Platform : EP/P027482/1

History

School

  • Mechanical, Electrical and Manufacturing Engineering

Published in

Computers in Industry

Volume

118

Pages

103223

Publisher

Elsevier

Version

AM (Accepted Manuscript)

Rights holder

© Elsevier

Publisher statement

This paper was accepted for publication in the journal Computers in Industry and the definitive published version is available at https://doi.org/10.1016/j.compind.2020.103223

Acceptance date

04/03/2020

Publication date

2020-03-20

Copyright date

2020

ISSN

0166-3615

eISSN

1872-6194

Language

en

Depositor

Dr Paul Goodall. Deposit date: 3 June 2020

Article number

103223