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dc.contributor.author | Karakostas, Anastasios | es_ES |
dc.contributor.author | Poler, R. | es_ES |
dc.contributor.author | Fraile Gil, Francisco | es_ES |
dc.contributor.author | Vrochidis, Stefanos | es_ES |
dc.date.accessioned | 2022-12-14T11:46:57Z | |
dc.date.available | 2022-12-14T11:46:57Z | |
dc.date.issued | 2021-06-09 | es_ES |
dc.identifier.isbn | 978-1-6654-1980-2 | es_ES |
dc.identifier.uri | http://hdl.handle.net/10251/190666 | |
dc.description.abstract | [EN] This paper presents a new innovative framework to support smart manufacturing quality assurance. More specifically, the i4Q framework provides an IoT-based Reliable Industrial Data Services (RIDS), a complete suite consisting of 22 innovative Solutions, able to manage the huge amount of industrial data coming from cheap cost-effective, smart, and small size interconnected factory devices for supporting manufacturing online monitoring and control. The i4Q Framework guarantees data reliability with functions grouped into five basic capabilities around the data cycle: sensing, communication, computing infrastructure, storage, and analysis-optimization. i4Q RIDS includes simulation and optimization tools for manufacturing line continuous process qualification, quality diagnosis, reconfiguration and certification for ensuring high manufacturing efficiency, leading to an integrated approach to zero-defect manufacturing. This paper presents the main principles of the i4Q framework and the relevant industrial case studies on which it will be evaluated. | es_ES |
dc.description.sponsorship | This project has received funding from the European Union's Horizon 2020 research and innovation programme under grant agreement No 958205 | es_ES |
dc.language | Inglés | es_ES |
dc.publisher | IEEE | es_ES |
dc.relation.ispartof | 2021 IEEE International Workshop on Metrology for Industry 4.0 & IoT (MetroInd4.0&IoT) | es_ES |
dc.rights | Reserva de todos los derechos | es_ES |
dc.subject | Product quality | es_ES |
dc.subject | Process quality | es_ES |
dc.subject | Data quality | es_ES |
dc.subject | Data reliability | es_ES |
dc.subject | Blockchain | es_ES |
dc.subject | Virtual sensors | es_ES |
dc.subject | Digital twins | es_ES |
dc.subject | Process simulation | es_ES |
dc.subject | Process optimization | es_ES |
dc.subject | Zero-defect manufacturing | es_ES |
dc.subject.classification | ORGANIZACION DE EMPRESAS | es_ES |
dc.title | Industrial Data Services for Quality Control in Smart Manufacturing - the i4Q Framework | es_ES |
dc.type | Comunicación en congreso | es_ES |
dc.type | Capítulo de libro | es_ES |
dc.identifier.doi | 10.1109/MetroInd4.0IoT51437.2021.9488490 | es_ES |
dc.relation.projectID | info:eu-repo/grantAgreement/EC/H2020/958205/EU | es_ES |
dc.rights.accessRights | Abierto | es_ES |
dc.contributor.affiliation | Universitat Politècnica de València. Escuela Politécnica Superior de Alcoy - Escola Politècnica Superior d'Alcoi | es_ES |
dc.description.bibliographicCitation | Karakostas, A.; Poler, R.; Fraile Gil, F.; Vrochidis, S. (2021). Industrial Data Services for Quality Control in Smart Manufacturing - the i4Q Framework. IEEE. 454-457. https://doi.org/10.1109/MetroInd4.0IoT51437.2021.9488490 | es_ES |
dc.description.accrualMethod | S | es_ES |
dc.relation.conferencename | IEEE International Workshop on Metrology for Industry 4.0 & IoT 2021 | es_ES |
dc.relation.conferencedate | Junio 07-09,2021 | es_ES |
dc.relation.conferenceplace | Online | es_ES |
dc.relation.publisherversion | https://doi.org/10.1109/MetroInd4.0IoT51437.2021.9488490 | es_ES |
dc.description.upvformatpinicio | 454 | es_ES |
dc.description.upvformatpfin | 457 | es_ES |
dc.type.version | info:eu-repo/semantics/publishedVersion | es_ES |
dc.relation.pasarela | S\447966 | es_ES |