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- W2941527439 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." @default.
- W2941527439 created "2019-05-03" @default.
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- W2941527439 date "2019-05-01" @default.
- W2941527439 modified "2023-10-03" @default.
- W2941527439 title "Towards a Decision-support Framework for Reducing Ramp-up Effort in Plug-and-Produce Systems" @default.
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- W2941527439 doi "https://doi.org/10.1109/icphys.2019.8780369" @default.
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