DC FieldValueLanguage
dc.contributorDepartment of Industrial and Systems Engineeringen_US
dc.creatorZheng, Pen_US
dc.creatorXia, Len_US
dc.creatorLi, Cen_US
dc.creatorLi, Xen_US
dc.creatorLiu, Ben_US
dc.date.accessioned2021-11-09T06:28:20Z-
dc.date.available2021-11-09T06:28:20Z-
dc.identifier.issn0278-6125en_US
dc.identifier.urihttp://hdl.handle.net/10397/91590-
dc.language.isoenen_US
dc.publisherElsevieren_US
dc.subjectIndustrial knowledge graphen_US
dc.subjectGraph embeddingen_US
dc.subjectCognitive manufacturingen_US
dc.subjectGraph neural networken_US
dc.subjectReinforcement learningen_US
dc.titleTowards Self-X cognitive manufacturing network : an industrial knowledge graph-based multi-agent reinforcement learning approachen_US
dc.typeJournal/Magazine Articleen_US
dc.identifier.spage16en_US
dc.identifier.epage26en_US
dc.identifier.volume61en_US
dc.identifier.doi10.1016/j.jmsy.2021.08.002en_US
dcterms.abstractEmpowered by the advanced cognitive computing, industrial Internet-of-Things, and data analytics techniques, today’s smart manufacturing systems are ever-increasingly equipped with cognitive capabilities, towards an emerging Self-X cognitive manufacturing network with higher level of automation. Nevertheless, to our best knowledge, the readiness of ‘Self-X’ levels (e.g., self-configuration, self-optimization, and self-adjust/adaptive/healing) is still in the infant stage. To pave its way, this work stepwise introduces an industrial knowledge graph (IKG)-based multi-agent reinforcement learning (MARL) method for achieving the Self-X cognitive manufacturing network. Firstly, an IKG should be formulated based on the extracted empirical knowledge and recognized patterns in the manufacturing process, by exploiting the massive human-generated and machine-sensed multimodal data. Then, a proposed graph neural network-based embedding algorithm can be performed based on a comprehensive understanding of the established IKG, to achieve semantic-based self-configurable solution searching and task decomposition. Moreover, a MARL-enabled decentralized system is presented to self-optimize the manufacturing process, and to further complement the IKG towards Self-X cognitive manufacturing network. An illustrative example of multi-robot reaching task is conducted lastly to validate the feasibility of the proposed approach. As an explorative study, limitations and future perspectives are also highlighted to attract more open discussions and in-depth research for ever smarter manufacturing.en_US
dcterms.accessRightsembargoed accessen_US
dcterms.bibliographicCitationJournal of manufacturing systems, Oct. 2021, v. 61, p. 16-26en_US
dcterms.isPartOfJournal of manufacturing systemsen_US
dcterms.issued2021-10-
dc.identifier.isiWOS:000705969500002-
dc.description.validate202111 bchyen_US
dc.description.oaNot applicableen_US
dc.identifier.FolderNumbera1047-n05-
dc.identifier.SubFormID43846-
dc.description.fundingSourceOthersen_US
dc.description.fundingTextNational Natural Research Foundation of China (No. 52005424), Mainland-Hong Kong Joint Funding Scheme (MHX/001/20), Innovation and Technology Commission, HKSAR, China, National Key R&D Programs of Cooperation on Scientific and Technological Innovation in Hong Kong, Macao and Taiwan (SQ2020YFE020182), and Jiangsu Provincial Policy Guidance Program (Hong Kong/Macau/Taiwan Science and Technology Cooperation, BZ2020049)en_US
dc.description.pubStatusPublisheden_US
dc.date.embargo2023.10.31en_US
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