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A framework to predict energy related key performance indicators of manufacturing systems at early design phase
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Assad, Fadi, Alkan, Bugra, Kaniappan Chinnathai, Malarvizhi, Ahmad, Mus'ab, Rushforth, Emma and Harrison, Robert (2019) A framework to predict energy related key performance indicators of manufacturing systems at early design phase. In: CIRP CMS 2019 , Ljubljana, Slovenia, 12-14 Jun 2019. Published in: Procedia CIRP, 81 pp. 145-150. doi:10.1016/j.procir.2019.03.026 ISSN 2212-8271.
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WRAP-framework-predict-energy-key-performance-indicators-Assad-2019.pdf - Accepted Version Embargoed item. Restricted access to Repository staff only - Requires a PDF viewer. Available under License Creative Commons Attribution Non-commercial No Derivatives 4.0. Download (1089Kb) |
Official URL: https://doi.org/10.1016/j.procir.2019.03.026
Abstract
Increasing energy prices, growing market competition, strict environmental legislations, concerns over global climate change and customer interaction incentivise manufacturing firms to improve their production efficiency and minimise bad impacts to environment. As a result, production processes are required to be investigated from energy efficiency perspective at early design phase where most benefits can be attained at low cost, time and risk. This article proposes a framework to predict energy-related key performance indicators (e-KPIs) of manufacturing systems at early design and prior to physical build. The proposed framework is based on the utilisation and incorporation of virtual models within VueOne virtual engineering (VE) tool and WITNESS discrete event simulation (DES) to predict e-KPIs at three distinct levels: production line, individual workstations and the components as individual energy consumption units (ECU). In this framework, alternative designs and configurations can be investigated and benchmarked in order to implement and build the best energy-efficient system. This ensures realising energy-efficient production system design while maintaining predefined production system targets such as cycle-time and throughput rate. The proposed framework is exemplified by a use case of a battery module assembly system. The results reveal that the proposed framework results meaningful e-KPIs capable of supporting manufacturing system designers in decision making in terms of component selection and process design towards an improved sustainability and productivity.
Item Type: | Conference Item (Paper) | ||||||
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Divisions: | Faculty of Science, Engineering and Medicine > Engineering > WMG (Formerly the Warwick Manufacturing Group) | ||||||
Journal or Publication Title: | Procedia CIRP | ||||||
Publisher: | Elsevier Inc. | ||||||
ISSN: | 2212-8271 | ||||||
Official Date: | 24 June 2019 | ||||||
Dates: |
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Volume: | 81 | ||||||
Page Range: | pp. 145-150 | ||||||
DOI: | 10.1016/j.procir.2019.03.026 | ||||||
Status: | Peer Reviewed | ||||||
Publication Status: | Published | ||||||
Access rights to Published version: | Open Access (Creative Commons) | ||||||
Date of first compliant deposit: | 21 May 2019 | ||||||
Date of first compliant Open Access: | 21 May 2019 | ||||||
Conference Paper Type: | Paper | ||||||
Title of Event: | CIRP CMS 2019 | ||||||
Type of Event: | Conference | ||||||
Location of Event: | Ljubljana, Slovenia | ||||||
Date(s) of Event: | 12-14 Jun 2019 | ||||||
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