A neural network-based data-driven local modeling of spotwelded plates under impact

Solving large structural problems with multiple complex localized behaviors is extremely challenging. To address this difficulty, both intrusive and non-intrusive Domain Decomposition Methods (DDM) have been developed in the past, where the refined model (local) is solved separately in its own space and time scales. In this work, the Finite Element Method (FEM) at the local scale is replaced with a data-driven Reduced Order Model (ROM) to further decrease computational time. The reduced model aims to create a low-cost, accurate and efficient mapping from interface velocities to interface forces and enable the prediction of their time evolution. The present work proposes a modeling technique based on the Physics-Guided Architecture of Neural Networks (PGANNs), which incorporates physical variables other than input/output variables into the neural network architecture. We develop this approach on a 2D plate with a hole as well as a 3D case with spot-welded plates undergoing fast deformation, representing nonlinear elastoplasticity problems. Neural networks are trained using simulation data generated by explicit dynamic FEM solvers. The PGANN results are in good agreement with the FEM solutions for both test cases, including those in the training dataset as well as the unseen dataset, given the loading type is present in the training set

Real-time tracking of crack propagation in active structures

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Real-time inverse crack tracking in uncertain microstructures using PGD-based model reduction and extended Kalman filtering

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A data-driven approach for the real-time modelling of spot-welded patches under impact using deep learning

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Multigrid model reduction method proposal for fast evolution problems of heterogeneous media

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Suivi d'une interface solide mobile au sein d'un écoulement diphasique par une méthode de frontière immergée

International audienceLa simulation numérique de l’interaction entre une structure en mouvement et un écoulement diphasique est un challenge important pour le monde des industries navale, nucléaire ou aéronautique. Une méthode de frontière immergée définissant le domaine fluide-structure comme un milieu poreux est présentée. Elle est implémentée au sein d’un code CFD volumes-finis dédié aux écoulements diphasiques et basé sur une approche bi-fluide. La structure est définie de manière lagrangienne à l’aide d’une porosité nulle sur une grille cartésienne. Par conséquent, le bilan des fractions volumiques de phases, le bilan de quantité de mouvement de chaque phase, et le bilan de masse sont corrigés de manière à reconstruire l’interface fluide-structure. La méthode dite de “porosité variable en temps et en espace” est évaluée sur différents cas mono- et diphasiques

Multigrid model reduction method proposal for fast evolution problems of heterogeneous media

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A review on fused deposition modeling materials with analysis of key process parameters influence on mechanical properties

International audienceFused deposition modeling (FDM) also called fused filament fabrication (FFF) is the most used additive manufacturing (AM) technology. The growing impact of AM is due to its various advantages and its applicability to many domains. Many research works have focused on the improvements of FDM technique and optimization of the mechanical properties in order to fabricate parts that can be used in realm industrial applications. In the present work, a review of materials used in the FDM process is proposed together with an analysis of the key parameters affecting their mechanical behaviors. In this framework, FDM materials have been classified into three groups: standard, composite, and smart materials. Previous works have clearly shown that the process parameters have a greater influence on parts made with standard materials than on those made with composites. The effect of the process parameters such as air gap, layer thickness, build orientation, raster orientation, and contour number is discussed in regard to the mechanical solicitations: tensile and compression, three- or four-point bending tests and fatigue. The impact of these process parameters on different material categories is also analyzed. This reveals the specificities related to each materials group. This work can be considered like a global insight for the comprehension of the process—structure—mechanical property relations of common FDM materials. Synthetic remarks and recommendations are proposed for future investigations

A data-driven approach for the real-time modelling of spot-welded patches under impact using deep learning

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