000130930 001__ 130930
000130930 005__ 20240202151703.0
000130930 0247_ $$2doi$$a10.1016/j.array.2022.100176
000130930 0248_ $$2sideral$$a136650
000130930 037__ $$aART-2022-136650
000130930 041__ $$aeng
000130930 100__ $$aZambrano, Valentina
000130930 245__ $$aIndustrial digitalization in the industry 4.0 era: Classification, reuse and authoring of digital models on Digital Twin platforms
000130930 260__ $$c2022
000130930 5060_ $$aAccess copy available to the general public$$fUnrestricted
000130930 5203_ $$aDigital Twins (DTs) are real-time digital models that allow for self-diagnosis, self-optimization and self-configuration without the need for human input or intervention. While DTs are a central aspect of the ongoing fourth industrial revolution (I4.0), this leap forward may be reserved for the established, large-cap companies since the adoption of digital technologies among Small and Medium-size Enterprises (SMEs) is still modest. The aim of the H2020 European Project ”DIGITbrain” is to support a modular construction of DTs by reusing their fundamental building blocks, i.e., the Models that describe the behavior of the DT, their associated Algorithms and the Data required for the evaluation. By offering these building blocks as a service via a DT Platform (a Digital Twin Environment), the technical barriers among SMEs to adopt these technologies are lowered. This paper describes how digital models can be classified, reused and authored on such DT Platforms. Through experimental analyses of three industrial cases, the paper exemplifies how DTs are employed in relation to product assembly of agricultural robots, polymer injection molding, as well as laser-cutting and sheet-metal forming of aluminum.
000130930 536__ $$9info:eu-repo/grantAgreement/EC/H2020/952071/EU/Digital twins bringing agility and innovation to manufacturing SMEs, by empowering a network of DIHs with an integrated digital platform that enables Manufacturing as a Service (MaaS)/DIGITbrain$$9This project has received funding from the European Union’s Horizon 2020 research and innovation program under grant agreement No H2020 952071-DIGITbrain
000130930 540__ $$9info:eu-repo/semantics/openAccess$$aby$$uhttp://creativecommons.org/licenses/by/3.0/es/
000130930 592__ $$a2.88$$b2022
000130930 593__ $$aComputer Science (miscellaneous)$$c2022$$dQ1
000130930 594__ $$a5.6$$b2022
000130930 655_4 $$ainfo:eu-repo/semantics/article$$vinfo:eu-repo/semantics/publishedVersion
000130930 700__ $$aMueller-Roemer, Johannes
000130930 700__ $$aSandberg, Michael
000130930 700__ $$aTalasila, Prasad
000130930 700__ $$aZanin, Davide
000130930 700__ $$aGorm Larsen, Peter
000130930 700__ $$aLoeschner, Elke
000130930 700__ $$aThronicke, Wolfgang
000130930 700__ $$aPietraroia, Dario
000130930 700__ $$aLandolfi, Giuseppe
000130930 700__ $$aFontana, Alessandro
000130930 700__ $$aLaspalas, Manuel
000130930 700__ $$aAntony, Jibinraj
000130930 700__ $$aPoser, Valerie
000130930 700__ $$aKiss, Tamas
000130930 700__ $$aBergweiler, Simon
000130930 700__ $$a{Pena Serna}, Sebastian
000130930 700__ $$0(orcid)0000-0001-6906-9143$$aIzquierdo, Salvador$$uUniversidad de Zaragoza
000130930 700__ $$aViejo, Ismael
000130930 700__ $$aJuan, Asier
000130930 700__ $$aSerrano, Francisco
000130930 700__ $$aStork, André
000130930 7102_ $$15001$$2600$$aUniversidad de Zaragoza$$bDpto. Ciencia Tecnol.Mater.Fl.$$cÁrea Mecánica de Fluidos
000130930 773__ $$g14 (2022), 100176 [12 pp.]$$tArray$$x2590-0056
000130930 8564_ $$s1054982$$uhttps://zaguan.unizar.es/record/130930/files/texto_completo.pdf$$yVersión publicada
000130930 8564_ $$s2514163$$uhttps://zaguan.unizar.es/record/130930/files/texto_completo.jpg?subformat=icon$$xicon$$yVersión publicada
000130930 909CO $$ooai:zaguan.unizar.es:130930$$particulos$$pdriver
000130930 951__ $$a2024-02-02-14:47:07
000130930 980__ $$aARTICLE