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Tool state assessment for reduction of life cycle environmental impacts of aluminium machining processes via infrared temperature monitoring
conference contributionposted on 2015-07-10, 15:01 authored by Alessandro Simeone, Elliot WoolleyElliot Woolley, Shahin RahimifardShahin Rahimifard
Modern industrial machining environments face new challenges in implementing process monitoring systems to improve energy efficiency whilst ensuring quality standards. A process monitoring methodology for tool state identification during milling of aluminium has been implemented through the utilisation of an infrared (IR) camera. A features extraction procedure, based on statistical parameters calculation, was applied to temperature data generated by the IR camera. The features were utilised to build a fuzzy c-means (FCM) based decision making support system utilising pattern recognition for tool state identification. The environmental benefits deriving from the application of the developed monitoring system, are discussed in terms of prevention of rework/rejected products and associated energy and material efficiency improvements.
This research has been funded by Engineering and Physical Science Research Council (EPSRC) in the UK and carried out as part of activities of Centre for Innovative Manufacturing in Industrial Sustainability.
- Mechanical, Electrical and Manufacturing Engineering
Published inThe 22nd CIRP Conference on Life Cycle Engineering
Pages526 - 531 (6)
CitationSIMEONE, A., WOOLLEY, E. and RAHIMIFARD, S., 2015. Tool state assessment for reduction of life cycle environmental impacts of aluminium machining processes via infrared temperature monitoring. The 22nd CIRP Conference on Life Cycle Engineering, Sydney, Australia. Procedia CIRP, 29, pp. 526 - 531.
PublisherElsevier / © The Authors
- VoR (Version of Record)
Publisher statementThis work is made available according to the conditions of the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0) licence. Full details of this licence are available at: https://creativecommons.org/licenses/by-nc-nd/4.0/
NotesThis paper was published by Elsevier under a Creative Commons CC BY-NC-ND license, details available at: http://creativecommons.org/licenses/by-nc-nd/4.0/