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Please use this identifier to cite or link to this item: https://dspace.lboro.ac.uk/2134/37415

Title: Towards a decision-support framework for reducing ramp-up effort in plug-and-produce systems
Authors: Zimmer, Melanie
Ferreira, Pedro
Danny, Paul
Al-Yacoub, Ali
Lohse, Niels
Gentile, Valerio
Keywords: Decision-support framework
Expert system
Issue Date: 2019
Publisher: © IEEE
Citation: ZIMMER, M. ... et al, 2019. Towards a decision-support framework for reducing ramp-up effort in plug-and-produce systems. 2nd IEEE International Conference on Industrial Cyber-Physical Systems (ICPS 2019), Taipei, Taiwan, 6-9 May 2019.
Abstract: Nowadays, shorter and more flexible production cycles are vital to meet the increasing customized product demand. As any delays and downtimes in the production towards time-to-market means a substantial financial loss, manufacturers take an interest in getting the production system to full utilization as quickly as possible. The concept of plug-and-produce manufacturing systems facilitates an easy integration process through embedded intelligence in the devices. However, a human still needs to validate the functionality of the system and more importantly must ensure that the required quality and performance is delivered. This is done during the ramp-up phase, where the system is assembled and tested first-time. System adaptations and a lack of standard procedures make the ramp-up process still largely dependent on the operator’s experience level. A major problem that currently occurs during ramp-up, is a loss of knowledge and information due to a lack of means to capture the human’s experience. Capturing this information can be used to facilitate future ramp-up cases as additional insights about change actions and their effect on the system could be revealed. Hence, this paper proposes a decision-support framework for plugand-produce assembly systems that will help to reduce the ramp-up effort and ultimately shorten ramp-up time. As an illustrative example, a gluing station developed for the European project openMOS is considered.
Description: This paper is closed access until it is published.
Sponsor: The reported work is part of the openMOS project partially funded by the European Commission as part of ECH2020-IA (GA 680735). Additional thanks go to the EPSRC Centre for Doctoral Training in Embedded Intelligence (grant number EP/L014998/1) for the funding of closely related work.
Version: Accepted for publication
URI: https://dspace.lboro.ac.uk/2134/37415
Publisher Link: http://www.icps19.org/
Appears in Collections:Closed Access (Mechanical, Electrical and Manufacturing Engineering)

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