A framework to predict energy related key performance indicators of manufacturing systems at early design phase

Assad, F., Alkan, B., Kaniappan Chinnathai, M., Ahmad, M, Rushforth, E. and Harrison, R. 2019. A framework to predict energy related key performance indicators of manufacturing systems at early design phase . 52nd CIRP Conference on Manufacturing Systems. Ljubljana, Slovenia 12 - 14 Jun 2019 Elsevier. https://doi.org/10.1016/j.procir.2019.03.026

TitleA framework to predict energy related key performance indicators of manufacturing systems at early design phase
AuthorsAssad, F., Alkan, B., Kaniappan Chinnathai, M., Ahmad, M, Rushforth, E. and Harrison, R.
TypeConference paper
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.

Year2019
Conference52nd CIRP Conference on Manufacturing Systems
PublisherElsevier
Publisher's version
License
CC BY-NC-ND 4.0
File Access Level
Open (open metadata and files)
Publication dates
Published24 Jun 2019
JournalProcedia CIRP
Journal citation81, pp. 145-150
ISSN2212-8271
Digital Object Identifier (DOI)https://doi.org/10.1016/j.procir.2019.03.026

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