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Title: Infrared monitoring of aluminium milling processes for reduction of environmental impacts
Authors: Simeone, Alessandro
Woolley, Elliot
Luo, Yang
Williams, Owain
Rahimifard, Shahin
Keywords: Infrared monitoring
Eco-intelligent manufacturing
Neural networks
Artificial intelligence
Issue Date: 2017
Publisher: IRJES
Citation: SIMEONE, A. ...et al., 2017. Infrared monitoring of aluminium milling processes for reduction of environmental impacts. International Refereed Journal of Engineering and Science (IRJES), 6 (6), pp. 8-21.
Abstract: In modern manufacturing contexts, process monitoring is an important tool aimed at ensuring quality standard fulfilment whilst maximising throughput. In this work, a monitoring system comprised of an infrared (IR) camera was employed for tool state identification and surface roughness assessment with the objective of reducing environmental impacts of a milling process. Two data processing techniques, based on statistical parameters and polynomial fitting, were applied to the temperature signal acquired from the IR camera during milling operations in order to extract significant features. These features were inputted to two different neural network based procedures: pattern recognition and fitting, for decision making support on tool condition and surface roughness evaluation respectively. These capabilities are discussed in terms of reducing waste products and energy consumption whilst further improving productivity.
Description: This work is made available according to the conditions of the Creative Commons Attribution 4.0 International (CC BY 4.0) licence. Full details of this licence are available at: http://creativecommons.org/licenses/by/4.0/
Sponsor: This research has been partially funded by the EPSRC Centre for Innovative Manufacturing in Industrial Sustainability (EP/I033351/1).
Version: Published
DOI: 2319-1821
URI: https://dspace.lboro.ac.uk/2134/24094
Publisher Link: https://doi.org/10.183x/B6610821
ISSN: 2319-183X
Appears in Collections:Published Articles (Mechanical, Electrical and Manufacturing Engineering)

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