000132858 001__ 132858
000132858 005__ 20240410085609.0
000132858 0247_ $$2doi$$a10.1007/s12289-024-01812-4
000132858 0248_ $$2sideral$$a137676
000132858 037__ $$aART-2024-137676
000132858 041__ $$aeng
000132858 100__ $$aAmmar, Amine
000132858 245__ $$aCasting hybrid twin: physics-based reduced order models enriched with data-driven models enabling the highest accuracy in real-time
000132858 260__ $$c2024
000132858 5060_ $$aAccess copy available to the general public$$fUnrestricted
000132858 5203_ $$aKnowing the thermo-mechanical history of a part during its processing is essential to master the final properties of the product. During forming processes, several parameters can affect it. The development of a surrogate model makes it possible to access history in real time without having to resort to a numerical simulation. We restrict ourselves in this study to the cooling phase of the casting process. The thermal problem has been formulated taking into account the metal as well as the mould. Physical constants such as latent heat, conductivities and heat transfer coefficients has been kept constant. The problem has been parametrized by the coolant temperatures in five different cooling channels. To establish the offline model, multiple simulations are performed based on well-chosen combinations of parameters. The space-time solution of the thermal problem has been solved parametrically. In this work we propose a strategy based on the solution decomposition in space, time, and parameter modes. By applying a machine learning strategy, one should be able to produce modes of the parametric space for new sets of parameters. The machine learning strategy uses either random forest or polynomial fitting regressors. The reconstruction of the thermal solution can then be done using those modes obtained from the parametric space, with the same spatial and temporal basis previously established. This rationale is further extended to establish a model for the ignored part of the physics, in order to describe experimental measures. We present a strategy that makes it possible to calculate this ignorance using the same spatio-temporal basis obtained during the implementation of the numerical model, enabling the efficient construction of processing hybrid twins.
000132858 536__ $$9info:eu-repo/grantAgreement/ES/DGA-FSE/T24-20R$$9info:eu-repo/grantAgreement/ES/MICINN-AEI/PID2020-113463RB-C31/AEI/10.13039/501100011033
000132858 540__ $$9info:eu-repo/semantics/openAccess$$aby-nc-nd$$uhttp://creativecommons.org/licenses/by-nc-nd/3.0/es/
000132858 655_4 $$ainfo:eu-repo/semantics/article$$vinfo:eu-repo/semantics/acceptedVersion
000132858 700__ $$aBen Saada, Mariem
000132858 700__ $$0(orcid)0000-0003-1017-4381$$aCueto, Elias$$uUniversidad de Zaragoza
000132858 700__ $$aChinesta, Francisco
000132858 7102_ $$15004$$2605$$aUniversidad de Zaragoza$$bDpto. Ingeniería Mecánica$$cÁrea Mec.Med.Cont. y Teor.Est.
000132858 773__ $$g17, 2 (2024), [18 pp.]$$pInt.J.Mater.Form.$$tInternational Journal of Material Forming$$x1960-6206
000132858 8564_ $$s287280$$uhttps://zaguan.unizar.es/record/132858/files/texto_completo.pdf$$yPostprint$$zinfo:eu-repo/date/embargoEnd/2025-01-24
000132858 8564_ $$s1800173$$uhttps://zaguan.unizar.es/record/132858/files/texto_completo.jpg?subformat=icon$$xicon$$yPostprint$$zinfo:eu-repo/date/embargoEnd/2025-01-24
000132858 909CO $$ooai:zaguan.unizar.es:132858$$particulos$$pdriver
000132858 951__ $$a2024-04-10-08:44:43
000132858 980__ $$aARTICLE