Towards a Decision-support Framework for Reducing Ramp-up Effort in Plug-and-Produce Systems
2019 IEEE International Conference on Industrial Cyber Physical Systems (ICPS)(2019)
摘要
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.
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关键词
decision-support framework,ramp-up,plug-and-produce,expert system,learning.
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