Yes / This paper presents an agent-based framework for reconfiguring modular assembly
systems using machine learning and system performance estimates based on previous
reconfigurations. During a reconfiguration, system integrators and engineers make changes to
the machine to meet new production requirements by increasing capacity or manufacturing
new product variants. The framework provides a method for automatically evaluating these
changes in terms of impact on the performance of the production system, and building a
knowledge base. Such knowledge is used to support future reconfigurations by recommending
changes that are likely to improve the performance based on previous reconfigurations. The
agent architecture of the framework has two levels, one for individual assembly stations and
one for the entire production line. Knowledge bases of changes are built and utilised at both
levels using machine learning and performance estimates. A prototype implementation of the
proposed framework has been evaluated on an assembly production system in an industrial
scenario. Preliminary results show that framework helps to reduce the time and resources
required to complete a system reconfiguration and reach the desired production objectives. / This work was supported by the SURE Research Projects Fund of the University of Bradford and the European Commission [grant agreement n. 314762].
| Identifer | oai:union.ndltd.org:BRADFORD/oai:bradscholars.brad.ac.uk:10454/19100 |
| Date | 26 July 2022 |
| Creators | Scrimieri, Daniele, Adalat, Omar, Afazov, S., Ratchev, S. |
| Source Sets | Bradford Scholars |
| Language | English |
| Detected Language | English |
| Type | Conference paper, Accepted manuscript |
| Rights | © 2022 The Authors. This is an open access article under the CC BY-NC-ND license., CC-BY-NC-ND |
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