Towards a decision-support framework for reducing ramp-up effort in plug-and-produce systems
conference contributionposted on 03.04.2019 by Melanie Zimmer, Pedro Ferreira, Paul Danny, Ali Al-Yacoub, Niels Lohse, Valerio Gentile
Any type of content contributed to an academic conference, such as papers, presentations, lectures or proceedings.
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.
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.
- Mechanical, Electrical and Manufacturing Engineering