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Optimized assembly design for resource efficient production in a multiproduct manufacturing system

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journal contribution
posted on 23.06.2016, 10:17 by Oliver Gould, Alessandro Simeone, James Colwill, Elliot Woolley, Roy Willey, Shahin Rahimifard
Resource efficiency is one of the greatest challenges for sustainable manufacturing. Material flow in manufacturing systems directly influences resource efficiency, financial cost and environmental impact. A framework for material flow assessment in manufacturing systems (MFAM) was applied to a complex multi-product manufacturing case study. This supported the identification of options to alter material flow through changes to the product assembly design, to improve overall resource efficiency through eliminating resource intensive changeovers. Alternative assembly designs were examined using a combination of intelligent computation techniques: k-means clustering, genetic algorithm and ant colony algorithm. This provided recommendations balancing improvement potential with extent of process modification impact.

Funding

This work was funded by the Engineering and Physical Sciences Research Council [grant number EP/I033351/1] as part of the Centre for Innovative Manufacturing in Industrial Sustainability.

History

School

  • Mechanical, Electrical and Manufacturing Engineering

Published in

10th CIRP Conference on Intelligent Computation in Manufacturing Engineering - CIRP ICME '16

Citation

GOULD, O. ...et al., 2017. Optimized assembly design for resource efficient production in a multiproduct manufacturing system. Procedia CIRP, 62, pp. 523–528.

Publisher

© The Authors. Published by Elsevier

Version

VoR (Version of Record)

Publisher statement

This 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/

Acceptance date

08/06/2016

Publication date

2017

Notes

This paper was presented at the 10th CIRP Conference on Intelligent Computation in Manufacturing Engineering - CIRP ICME '16, Naples.

eISSN

2212-8271

Language

en

Location

Naples, Italy

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