Paper Info
Title
Towards a Decision-support Framework for Reducing Ramp-up Effort in Plug-and-Produce Systems
Abstract
Nowadays, shorter and more flexible production cycles are vital to meet the increasing customised 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 utilisation 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. Acquiring this information can be used to simplify 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 plug-and-produce assembly systems that will help to reduce the ramp-up effort and ultimately shorten ramp-up time. As an illustrative example, a glueing station developed as part of the European project openMOS is considered.
Year
DOI
Venue
2019
10.1109/ICPHYS.2019.8780369
2019 IEEE International Conference on Industrial Cyber Physical Systems (ICPS)
Keywords
DocType
ISBN
decision-support framework,ramp-up,plug-and-produce,expert system,learning.
Conference
978-1-5386-8501-3
Citations 
PageRank 
References 
0
0.34
7
Authors
6
Name
Order
Citations
PageRank
Melanie Zimmer100.68
Pedro S. Ferreira2225.89
Paul Danny300.68
Ali Al-Yacoub400.68
Niels Lohse55112.44
Valerio Gentile600.34