A practical approach for uncertainty management in rubber manufacturing processes using physics-informed real-time models

Viejo, Ismael ; Izquierdo, Salvador (Universidad de Zaragoza) ; Conde, Ignacio ; Zambrano, Valentina ; Alcalá, Noelia ; Gracia, Leticia A.

Resumen: Industrial manufacturing management can benefit from the use of modeling. For a correct representation of the manufacturing process and the subsequent management, the models must incorporate the effect of the uncertainty propagation throughout the stages considered. In this paper, the proposed methodology for uncertainty management uses a nonintrusive method that is based on building a deterministic physics-informed real-time model for the a posteriori computation of output uncertainties. This model is built using tensor factorization as the Model Order Reduction technique. It includes as model parameters: material properties, process operations, and those random and epistemic uncertainties of known variables. The resulting model is used off-line to identify sensitivities and therefore to unify uncertainty management across the material transformation process. This method is presented by its direct application to an automotive door seal manufactured by continuous co-extrusion of several rubbers and reinforcement (metal strip and glass fiber thread).

Idioma: Inglés

DOI: 10.3390/polym14102049

Año: 2022

Publicado en: Polymers 14, 10 (2022), 2049 [13 pp.]
ISSN: 2073-4360

Factor impacto JCR: 5.0 (2022)

Categ. JCR: POLYMER SCIENCE rank: 16 / 85 = 0.188 (2022) - Q1 - T1

Factor impacto CITESCORE: 6.6 - Chemistry (Q1) - Materials Science (Q1)

Factor impacto SCIMAGO: 0.72 - Polymers and Plastics (Q1) - Chemistry (miscellaneous) (Q1)

Financiación: info:eu-repo/grantAgreement/EC/H2020/723082/EU/Non-intrusive Sparse Subspace Learning for Parametrized Problems/STREAM-0D

Tipo y forma: Article (Published version)

Área (Departamento): Área Mecánica de Fluidos (Dpto. Ciencia Tecnol.Mater.Fl.)