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Improving the Cyber-Resilience and the Trust of Distributed Infrastructures
No full textInternational audienc
Introduction à la mécanique et la thermodynamique des milieux continus
No full textInternational audienceDoctora
Digital Twins, Extended Reality, and Artificial Intelligence in Manufacturing Reconfiguration: A Systematic Literature Review
No full textInternational audienceThis review draws on a systematic literature review and bibliometric analysis to examine how Digital Twins (DTs), Extended Reality (XR), and Artificial Intelligence (AI) support the reconfiguration of Cyber–Physical Systems (CPSs) in modern manufacturing. The review aims to provide an updated overview of these technologies’ roles in CPS reconfiguration, summarize best practices, and suggest future research directions. In a two-phase process, we first analyzed related work to assess the current state of assisted manufacturing reconfiguration and identify gaps in existing reviews. Based on these insights, an adapted PRISMA methodology was applied to screen 165 articles from the Scopus and Web of Science databases, focusing on those published between 2019 and 2025 addressing DT, XR, and AI integration in Reconfigurable Manufacturing Systems (RMSs). After applying the exclusion criteria, 38 articles were selected for final analysis. The findings highlight the individual and combined impact of DTs, XR, and AI on reconfiguration processes. DTs notably reduce reconfiguration time and improve system availability, AI enhances decision-making, and XR improves human–machine interactions. Despite these advancements, a research gap exists regarding the combined application of these technologies, indicating potential areas for future exploration. The reviewed studies recognized limitations, especially due to diverse study designs and methodologies that may introduce risks of bias, yet the review offers insight into the current DT, XR, and AI landscape in RMS and suggests areas for future research
Magnesium Nanoparticles: A Biocompatible and Sustainable Alternative to Gold for Advanced Plasmonic Applications
No full textInternational audienceGold nanoparticles (GNPs) have drawn considerable interest for their plasmonic properties, which are highly relevant for applications in chemical and biochemical sensing for trace molecule detection, tumor ablation, and Lab-On-Chip technologies. In this study, we introduce a novel fabrication method for GNPs that leverages polymer self-assembly to produce precisely shaped, uniform nanoparticles. Here, we outline the synthetic mechanism that enables controlled self-assembly to obtain nanoparticles with well-defined morphologies, including spherical, cubic, and octahedral shapes.Our approach allows for successful alteration of nanoparticle shapes directly during synthesis, in contrast to conventional multi-step chemical methods. By spin-coating a dispersion of gold precursor within a homopolymer onto a conductive substrate, we achieve direct control over nanoparticle shape and size. Beyond structural and extinction analyses, we use ellipsometry to characterize both the experimental and theoretical optical properties of these GNPs, providing a comprehensive understanding of their unique functional characteristics.</p
Pore Network 3D Reconstruction for Clays Using FIB-SEM Images
No full textInternational audienceThe aim of this study is to propose a new approach to explore the 3D evolution of pore properties in a saturated and remoulded clay in relation to mechanical loading. Oedometric loading until the desired stress level was applied to a clay sample. A post-mortem observation using scanning electron microscopy (SEM) coupled with focused ion beam (FIB) was used to obtain the 3D microstructure of a micro-volume of the sample. A novel processing method, incorporating machine learning for pore segmentation was performed to reconstitute the micro-volume. Pore network properties were studied through different parameters like orientation and morphology. The results obtained and the method developed in this study highlight the significant potential of 3D FIB-SEM observations in characterizing the microstructures of saturated clays
Les traitements laser sous vide poussé améliorent la résistance, la ductilité et la limite de fatigue de l'acier inoxydable fabriqué de manière additive
No full textPost-process laser scanning under high vacuum is proposed as a non-isothermal heat treatment to simultaneously refine the intragranular microstructure near the surface and reduce surface roughness, while also preventing oxidation, in order to enhance the overall mechanical response of an alloy. This treatment is performed using laser spot sizes and scan speeds that produce higher temperature gradients and faster heating/cooling rates than those encountered during manufacturing. The effectiveness of this approach is demonstrated on laser-based direct energy deposited (LDED) 316L stainless steel using parameters similar to those used in laser-based powder bed fusion (LPBF). High vacuum (< 0.1 Pa) lasering is conducted inside a newly integrated continuous-wave laser and scanning electron microscope (CW Laser-SEM). The treatments result in an order-of-magnitude reduction in microsegregation cell and dislocation structure sizes, as well as in the surface roughness of LDED 316L. For a parameter set in which the laser penetrates 14% of the total depth (7% each on the two widest surfaces of dogbone-shaped samples), significant improvements are obtained in yield strength (31.11%), ductility (14.2%), and fatigue limit (25%). The proposed approach has tremendous potential to alter the microstructure and improve the mechanical response of both additively and conventionally manufactured alloys
Single atom convolutional matching pursuit: Theoretical framework and application to Lamb waves based structural health monitoring
No full textInternational audienceLamb Waves (LW) based Structural Health Monitoring (SHM) aims to monitor the health state of thin structures. An Initial Wave Packet (IWP) is sent in the structure and interacts with boundaries, discontinuities, and with eventual damages thus generating many wave packets. An issue with LW based SHM is that at least two LW dispersive modes simultaneously exist. Matching Pursuit Method (MPM), which approximates a signal as a sum of delayed and scaled atoms taken from a known dictionary, is limited to nondispersive signals and relies on a priori known dictionary and is thus inappropriate for LW-based SHM. Single Atom Convolutional MPM, which addresses dispersion by decomposing a signal as delayed and dispersed atoms and limits the learning dictionary to only one atom, is alternatively proposed here. Its performances are demonstrated on numerical and experimental signals and it is used for damage monitoring. Beyond LW-based SHM, this method remains very general and applicable to a large class of signal processing problems
A crystal plasticity-damage coupled finite element framework for predicting mechanical behavior and ductility limits of thin metal sheets
No full textInternational audienceA new crystal plasticity finite element (CPFE) approach is developed to predict the mechanical behavior and ductility limits of thin metal sheets. Within this approach, a representative volume element (RVE) is chosen to accurately capture the mechanical characteristics of these metal sheets. This approach uses the periodic homogenization multiscale scheme to ensure the transition between the RVE and single crystal scales. At the single crystal scale, the mechanical behavior is modeled as elastoplastic within the finite strain framework. The plastic flow is governed by a modified version of the Schmid law, which incorporates the effects of damage on the evolution of microscopic mechanical variables. The damage behavior is modeled using the framework of Continuum Damage Mechanics (CDM), introducing a scalar microscopic damage variable at the level of each crystallographic slip system (CSS). The evolution law of this damage variable is derived from thermodynamic forces, resulting in deviations from the normality rule in microscopic plastic flow. This coupling of damage and elastoplastic behavior leads to a highly nonlinear set of constitutive equations. To solve these equations, an efficient return-mapping algorithm is developed and implemented in the ABAQUS/Standard finite element software via a user-defined material subroutine (UMAT). At the macroscopic scale, the onset of localized necking is predicted by the Rice bifurcation theory. The proposed damage-coupled single crystal model and its integration scheme are validated through several numerical simulations. The analysis extensively explores the impact of microstructural and damage parameters on the mechanical behavior and ductility limits of both single crystals and polycrystalline aggregates. The numerical results indicate that both of the mechanical behavior and ductility limits are significantly influenced by the microscopic damage and deviations from normal plastic flow rule
Elasticity-inspired data-driven micromechanics theory for unidirectional composites with interfacial damage
No full textInternational audienceWe present a novel elasticity-inspired data-driven Fourier homogenization network (FHN) theory for periodic heterogeneous microstructures with square or hexagonal arrays of cylindrical fibers. Towards this end, two custom-tailored networks are harnessed to construct microscopic displacement functions in each phase of composite materials, based on the exact Fourier series solutions of Navier’s displacement differential equations. The fiber and matrix networks are seamlessly connected through a common loss function by enforcing the continuity conditions, in conjunction with periodicity boundary conditions, of both tractions and displacements. These conditions are evaluated on a set of weighted collocation points located on the fiber/matrix interface and the exterior faces of the unit cell, respectively. The partial derivatives of displacements are computed effortlessly through the automatic differentiation functionality. During the training of the FHN model, the total loss function is minimized with respect to the Fourier series parameters using gradient descent and concurrently maximized with respect to the adaptive weights using gradient ascent. The transfer learning technique is employed to speed up the training of new geometries by leveraging a pre-trained model. Comparison with finite-element/volume-based unit cell solutions under various loading scenarios showcases the computational capability of the proposed method. The utility of the proposed technique is further demonstrated by capturing the interfacial debonding in unidirectional composites via a cohesive interface model
Usages de l’IA et biais cognitifs : le cas des systèmes navals de défense
No full textAt a time of increasing use of Artificial Intelligence and where research centers, schools and general staff are thinking about defense innovation and the integration of AI into defense systems, Léo Facca and Denis Lemaître (École Navale) are questioning the cognitive biases specific to these technologies and their use, through the prism of naval defense systems