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This paper aims at introducing the main building blocks of a digital twin, embracing physics-based and data-driven functionalities, both enriching mutually. Both should proceed in almost real-time, and the last being able to proceed in the scarce data limit. When applied to materials and processes, model order reduction technologies enable the construction of the so-called “computational vademecum”, whereas data-driven modelling, based in advanced regressions, must be informed by the physics to encompass rapidity and accuracy, in the low data limit. Despite of the recent advances, a lot of functionalities are needed and are under progress, some of them representing real scientific challenges. A number of them, the ones that we estimate being the most crucial, will be discussed in the present work.

Most suspension descriptions nowadays employed are based on the Jeffery's model andsome phenomenological adaptations of it that do not take into account size effects, that is, the kinematicsand stresses do not introduce a micro-mechanical characteristic length and thus, the rheologicalproperties become independent of the rod length. New models able to enrich first gradient kinematicsas well as to activate rod-bending mechanisms (needed for explaining the mild elasticity experimentallynoticed) are needed. In this paper we propose a second gradient description able to activate rods bending.

A new efficient updated-Lagrangian strategy for numerical simulations of material forming processes is presented in this work. The basic ingredients are the in-plane-out-of-plane PGD-based decomposition and the use of a robust numerical integration technique (the Stabilized Conforming Nodal Integration). This strategy is of general purpose, although it is especially well suited for plateshape geometries. This paper is devoted to show the feasibility of the technique through some simple numerical examples.

Forming processes usually involve irreversible plastic transformations. The calculation in that case becomes cumbersome when large parts and processes are considered. Recently Model Order Reduction techniques opened new perspectives for an accurate and fast simulation of mechanical systems, however nonlinear history-dependent behaviors remain still today challenging scenarios for the application of these techniques. In this work we are proposing a quite simple non intrusive strategy able to address such behaviors by coupling a separated representation with a POD-based reduced basis within an incremental elastoplastic formulation.

This paper is concerned with the prediction of part distortion induced by residual stress relief during machining. Aeronautical manufacturing requires nowadays the production of large monolithic parts from workpieces obtained by forging or rolling operations. Residual stresses induced during these forming steps are often consequent and represent a significant obstacle for manufacturers. Re-equilibration of initial residual stresses induced by material removal causes the part to deform.The method presented in this paper provides a tool for predicting changes in the geometry of the part occurring during machining and after unclamping. This prediction is based on an on-line data acquisition. From the predicted shape, the manufacturer is able to anticipate extra conforming steps eventually required afterwards and an updated toolpath accounting for geometrical changes happening during the on-going operation can be derived.The impact of the unknown initial residual stresses on the part deformation during machining is predicted on-line by comparing measured deformations to a reduced basis of mechanically admissible evolutions for the part geometry induced by material removal. This reduced basis is extracted from a larger database constructed off-line thanks to machining simulations and observations of previously machined similar parts.An auxiliary database for post-machining distortion is constructed similarly and makes possible a quick and reliable on-line prediction of the final shape of the part (i.e. after unclamping).

The aircrafts fuselage structure is usually composed of an assembly of stringers and frames made of cold-worked aluminium profiles. In particular, frames need of a forming process that shapes the profile into the frame’s curved shape. To do this, both profile ends are clamped, and then the profile is simultaneously stretched and pressed against the mould so that the material is plastically deformed. Industrial experience shows that most of times the resultant frame does not fulfil neither curvature nor planarity tolerances. These defects are mainly due to spring-back, residual stresses, and some technologic restrictions related to the machinery. The lack of understanding has led industry to reduce the automation level, and thus the forming process is frequently interrupted to perform verifications and adjustments that make the process to be time-consuming and very much dependent on the know-how of the machine operator. Aiming to improve the frame’s industrialisation, this work first analyses the influence of several parameters in the final shape. Then, we propose a computer-aided forming process based on the concept of Computational Vademecum (CV), which is also introduced in this work. It allows reducing the dependence on the operator know-how, while reliability and repeatability of the process can be improved.

LCM simulation is computationally expensive because it needs an accurate solution of flowequations during the mold filling process. When simulating large computing times are not compatiblewith standard optimization techniques (for example for locating optimally the injection nozzles)or with process control that in general requires fast decision-makings. In this work, inspired by theconcept of medial axis, we propose a numerical technique that computes numerically approximatedistance fields by invoking computational geometry concepts that can be used for the optimal locationof injection nozzles in infusion processes. On the other hand we also analyze the possibilities thatmodel order reduction offers to fast and accurate solutions of flow models in mold filling processes.

Resin Infusion (RI) process is one of the common techniques used in the industry for large composite parts production. This technique uses vacuum pressure to drive the resin into a laminate. Preform is laid dry into the mold and the vacuum is applied before the resin is introduced. Once a complete vacuum is achieved, resin is sucked into the laminate via placed tubing. Fig.1 shows a diagram of this process.An appropriate modeling of flow front’s shapes constrained by LRI process during filling can be based on the continuous deformation of the vent oriented flow pattern due to the driving pressure from the inlet. One of the main objectives is that the flow achieves the contour vent uniformly to avoid pressure drop and ensuring complete filling. In LRI, the flow front shape progression is mainly conditioned by the initial arrangement of the injection line allocation and the permeability of the preform that can evolve along the mold

Friction Stir Welding (FSW) is a welding technique the more and more demanded in industry by its multiple advantages. Despite its wide use, its physical foundations and the effect of the process parameters have not been fully elucidated. Numerical simulations are a powerful tool to achieve a greater understanding in the physics of the problem. Although several approaches can be found in the literature for simulating FSW, all of them present different limitations that restrict their applicability in industrial applications. This paper presents a new solution strategy that combines a robust approximation method, based on natural neighborhood interpolation, with a solution separated representation making use of the Proper Generalized Decomposition (PGD), for creating a new 3D updated-Lagrangian strategy for addressing the 3D model while keeping a 2D computational complexity

Real-time control of manufacturing processes is a challenging issue for nowadays industry. The need for ever more efficient production requires new strategies in order to make correct decisions in an acceptable time. In a large number of cases, operators working on a CNC machine tool have a reduced number of possibilities for interacting in real-time with the machine. Numerical simulation based control is in that sense an appealing alternative to the conventional approach since it provides the operator with an additional source of information, confirming his choices or in reverse suggesting a more adapted strategy. The main goal of this work is to propose a method to move from a bilateral approach (operator and CNC controller) to a trilateral one where the simulation is an active component of the manufacturing process. This paper focuses on a simple issue sometimes encountered in milling processes: how to remove a constant thickness of material at the surface of a part whose exact geometry is unknown? The difficulty lies in the choice of an appropriate trajectory for the tool. So far the method which is employed consists in acquiring the geometry of the part thanks to a palpation step made prior to milling. However, this step has to be repeated for each part and can become rather fastidious as the size of the part increases. The approach presented here gets rid of the palpation step and makes use of online measurements for identifying the real geometry and correcting the trajectory of the tool in accordance. By monitoring the forces applying on the tool (directly on the NC), we have access to the milling depth and therefore to the geometry of the part at several locations along the trajectory of the tool. This information is used as an input data for our numerical model running on an external device, which finally derives an approximation for the geometry. An optimized trajectory is then obtained and is updated on the machine. This procedure is repeated as the tool moves forward and it allows for a fast and robust on-line correction of the toolpath.

International audience; This paper proposes a first attempt to define a two scales kinetic theory to describe concentrated suspensions involving short fibers, nano-fibers or nanotubes. In this case, fiber-fiber interactions can not be neglected and rich microstructures issued from these interactions can be observed, involving a diversity of fibers clusters or aggregates with complex kinematics, and different sizes and shapes. These clusters can interact to create larger clusters and also break because the flow induced hydrodynamic forces. In this paper we propose a double-scale model to describe such microstructure: at the finest scale we study the cluster kinematic based on the behaviour of the rods that constitute it, at a coarser scale, we use clusters distribution to derive the effect of the clusters presence on the suspensions properties.

We analyse here how Dynamic Data Driven Application Systems (DDDAS) can constitute a valuable tool in the field of forming processes. Simulation tools in the field of DDDAS are required to provide a response in real-time, a requisite that is often too severe for complex problems. Here, we consider that of hyperelasticity, commonly used in different fields such as rubber manufacturing or surgery, for instance. We analyse here how model reduction techniques, and particularly Proper Generalized Decompositions (PGD) methods can provide a suitable response to the strong requirements posed by DDDAS. We will consider two different approaches to the problem. The first one is an explicit approach that nevertheless provides with good results. The second one is based on the use of Asymptotic Numerical Method.

Matrices employed in composites parts of aeronautic structures consist of a thermoset / thermoplastic mixture. Thermoplastic is introduced in low concentration in order to improve the mechanical properties, in particular the ones related to choc resistance. However, there are two antagonist mechanisms, the one related to energy that leads to demixing and the one related to entropy that tends to mix. These effects are strongly coupled with the elasticity of thermoplastic, the evolution from a newtonian fluid to a viscoelastic one of thermosets, the presence of reinforcement fibers, … and are nowadays bad understood despite the significant impact that these effects have on the composite microstructure and then on its mechanical properties. This work constitutes a first attempt to understand these complex physics.

Many models encountered in computer science remain intractable because of their tremendouscomplexity. Among them, the numerical modeling of manufacturing processes involving severalcharacteristic times is a challenging issue. Classical incremental methods often fail for solving efficientlysuch transient models. In that sense model reduction based simulation appears to be a verypromising alternative. Multidimensional parametric models can be solved within the context of theProper Generalized Decomposition (PGD). It opens new horizons regarding time parallelization. Indeed,by no more considering the initial condition of a transient problem as a static input data butas an extra- coordinate similarly to space and time, we demonstrate that it is possible to parallelizeefficiently the computation and even reach real-time in some cases.