Advanced modeling and simulation of sheet moulding compound (SMC) processes
TypeArticles dans des revues avec comité de lecture
In SMC processes, a charge of a composite material, which typically consists of a matrix composed of an unsaturated polyester or vinylester, reinforced with chopped glass fibres or carbon fi bre bundles and fillers, is placed on the bottom half of the preheated mould. The charge usually covers 30 to 90% of the total area. The upper half of the mould is closed rapidly at a speed of about 40 mm/s. This rapid movement causes the charge to flow inside the cavity. The reinforcing fibres are carried by the resin and experience a change of confi guration during the flow. This strongly influences the mechanical properties of the final part. Several issues compromises its efficient numerical simulation, among them: (i) the modeling of flow kinematics able to induce eventual fibres/resin segregation, (ii) the con ned fibres orientation evolution and its accurate prediction, (iii) local dilution effects, (iv) flow bifurcation at junctions and its impact on the fibres orientation state, (v) charge / mould contact and (vi) parametric solutions involving non-interpolative fields. The present paper reports advanced modeling and simulation techniques for circumventing, or at least alleviating, the just referred difficulties.
Showing items related by title, author, creator and subject.
GHNATIOS, Chady; ABISSET-CHAVANNE, Emmanuelle; AMMAR, Amine; CUETO, Elías; DUVAL, Jean Louis; CHINESTA, Francisco (Elsevier B.V., 2019)This work aims at proposing a new procedure for parametric problems whose separated representation has been considered difficult, or whose SVD compression impacted the results in terms of performance and accuracy. The ...
IBÁÑEZ, Rubén; ABISSET-CHAVANNE, Emmanuelle; GONZÁLEZ, David; DUVAL, Jean Louis; CUETO, Elias; CHINESTA, Francisco (Springer-Verlag France, 2019)In recent times a growing interest has arose on the development of data-driven techniques to avoid the employ of phenomenological constitutive models. While it is true that, in general, data do not fit perfectly to existing ...
A Multidimensional Data-Driven Sparse Identification Technique: The Sparse Proper Generalized Decomposition IBAÑEZ, Rubén; ABISSET-CHAVANNE, Emmanuelle; AMMAR, Amine; GONZALEZ, David; CUETO, Elias; HUERTA, Antonio; DUVAL, Jean Louis; CHINESTA, Francisco (Hindawi Limited, 2018)Sparse model identification by means of data is especially cumbersome if the sought dynamics live in a high dimensional space. This usually involves the need for large amount of data, unfeasible in such a high dimensional ...
Some applications of compressed sensing in computational mechanics: model order reduction, manifold learning, data-driven applications and nonlinear dimensionality reduction IBAÑEZ, R.; ABISSET-CHAVANNE, Emmanuelle; CUETO, Elías G.; AMMAR, Amine; DUVAL, Jean Louis; CHINESTA, Francisco (Springer, 2019)Compressed sensing is a signal compression technique with very remarkable properties. Among them, maybe the most salient one is its ability of overcoming the Shannon–Nyquist sampling theorem. In other words, it is able to ...
Structural health monitoring by combining machine learning and dimensionality reduction techniques QUARANTA, Giacomo; LOPEZ, Elena; ABISSET-CHAVANNE, Emmanuelle; DUVAL, Jean Louis; HUERTA, Antonio; CHINESTA, Francisco (Scipedia S.L., 2019)Structural Health Monitoring is of major interest in many areas of structural mechanics. This paper presents a new approach based on the combination of dimensionality reduction and data-mining techniques able to differentiate ...