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dc.contributor.authorAdalat, Omar
dc.contributor.authorTalal, Muhammad
dc.contributor.authorAli Cherif, Mohammed A.
dc.contributor.authorScrimieri, Daniele
dc.date.accessioned2022-08-23T10:56:19Z
dc.date.accessioned2022-09-21T07:58:55Z
dc.date.available2022-08-23T10:56:19Z
dc.date.available2022-09-21T07:58:55Z
dc.date.issued2022-09
dc.identifier.citationAdalat O, Talal M, Ali Cherif M et al (2022) Model-based generation of manufacturing process plans through incremental topology formation. 21st UK Workshop on Computational Intelligence. 7-9 Sep 2022. Sheffield, UK.en_US
dc.identifier.urihttp://hdl.handle.net/10454/19136
dc.descriptionYesen_US
dc.description.abstractIn advanced manufacturing systems, the production of complex and highly customisable products requires the preparation of many different product specifications and associated manufacturing process plans. The creation of these plans involves the search for the production resources (e.g. robots, machine tools, inspection devices) that are needed to implement the product specifications and how to orchestrate them. We present a model-based approach to the automatic generation of manufacturing process plans from the models of the target products and available resources. The modelling language is based on labelled transition systems, which are useful to represent sequences of operations that can be executed in parallel by multiple production resources. Some preliminary experimental results demonstrate the feasibility of the presented approach.en_US
dc.language.isoenen_US
dc.publisherSpringer
dc.rights(c) 2022 The Authors.en_US
dc.subjectManufacturing processen_US
dc.subjectProcess planen_US
dc.subjectControlleren_US
dc.subjectRealisabilityen_US
dc.subjectLabelled transition systemen_US
dc.subjectRoboticsen_US
dc.titleModel-based generation of manufacturing process plans through incremental topology formationen_US
dc.status.refereedYesen_US
dc.typeConference paperen_US
dc.type.versionAccepted manuscripten_US
dc.description.publicnotesThis conference paper will be released for public view at the end of the 12-month embargo period established by SpringerNature for their book series.en_US
dc.rights.licenseUnspecifieden_US
dc.date.updated2022-08-23T10:56:21Z
refterms.dateFOA2022-09-21T07:59:19Z
dc.openaccess.statusembargoedAccessen_US


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