Publications

Since 2020, the health crisis has profoundly disrupted higher education, initiating an accelerated transition to digital teaching practices. This change, initially forced upon us, has become part of an ongoing reflection on flexibility, inclusivity, and pedagogical innovation. The AtriuM Learning Center project is part of the École nationaleArts et Métiers the Evolutive Learning Factories ELF) program and the Direction de l'Information Scientifique et de la Science Ouverte (DISSO). Its development, particularly on the Arts et Métiers campus in Metz, illustrates this transformation by offering a connected and evolving learning space. Drawing on international best practices in university learning centers and in-depth surveys to understand the needs of future users, AtriuM Metz is based on: - A modular space integrating digital and emerging technologies, manufacturing workshops, and environments adapted to various educational needs. - A one-stop shop for teachers, researchers, staff, and external users, centralizing services such as the loan of resources and connected objects, targeted training, and the provision of equipped spaces that promote collaborative or individual work. This space embodies a concrete response to the challenges posed by emerging technologies, particularly virtual reality (VR) and generative artificial intelligence (AI). These tools immerse students in innovative learning scenarios while raising ethical and educational questions, such as the preservation of human interaction and critical thinking. Beyond support and training, AtriuM plays a key role in scientific dissemination, organizing conferences and workshops to bring research closer to civil society. By raising awareness of sustainability issues and technological skills, it contributes to a resilient and inclusive pedagogy. This article on the AtriuM project demonstrates the ability of higher education to evolve in the face of contemporary challenges, marking a new era in which technology, space, and pedagogy come together to anticipate the needs of the 21st century.

Reliable detection of barely visible impact damage is critical to ensure the structural integrity of composite components in service, particularly in safety-critical applications such as pressure vessels and transportation systems. This study presents a solution for detecting such damage in woven glass fiber-reinforced thermoplastic composites using terahertz (THz) time-of-flight tomography and convolutional neural networks. THz provides non-contact, non-ionizing, high-axial-resolution imaging of subsurface and back-surface damage, addressing key limitations of surface-based inspection methods. While THz imaging alone may not always permit conclusive damage identification, we bridge this gap by training neural network classifiers on depth-resolved THz B-scan images using ground truth from co-located X-ray micro-computed tomography. Among several pretrained architectures tested via transfer learning, DenseNet-121 exhibits the highest accuracy. The model remains robust even when trained on truncated B-scans excluding surface indentation features, confirming its ability to detect structural anomalies located internally or on the back surface. This is particularly relevant for applications where back-side access is not feasible. Experimental validation is performed on impacted glass-fiber-reinforced thermoplastic coupons prepared in accordance with ASTM D7136, with damage severity quantified through force–displacement data and micro-tomographic analysis. Labeling for supervised learning conforms to acceptance criteria from industrial standards for composite pressure vessels (ASME BPVC Section X, CGA C-6.2), ensuring regulatory alignment and enabling deployment in quality control workflows. The proposed method minimizes the need for expert interpretation or secondary validation and offers direct applicability to in-service inspection and manufacturing quality control.

The work-hardening curve of sheet metals under large plastic strains can be extracted from the Plane Strain Compression Test (PSCT) using an analytical method that relies on several simplifying assumptions and correction factors (friction, boundary conditions, lateral spreading, tool geometry, yield criterion, anisotropy). This study rigorously assesses each of these correction factors using finite element simulations. Synthetic materials with predefined hardening laws are used to enable direct comparison between the reference curves and those extracted from simulated PSCTs. Dedicated simulation setups were developed to isolate the effect of each factor through progressive 2D and 3D configurations. The results show that the analytical method is generally valid when appropriate corrections are applied, with improved accuracy observed when using rounded tools with small radii under low-friction conditions. Recommendations for the selection of correction factors are provided to enhance the reliability of flow curves obtained through this method.

Elasticipy is a Python library designed to streamline computation and manipulation of elasticity tensors for materials and crystalline materials, taking their specific symmetries into account. It provides tools to manipulate, visualize, and analyze tensors—such as stress, strain, and stiffness tensors—simplifying workflows for materials scientists and engineers.

A micromechanics-informed neural network framework is developed for homogenization of periodic unidirectional thermoconductive composites with cylindrically orthotropic fibers. The framework hard-imposes the steady-state governing heat conduction equations within the network architecture, enabling accurate capture of singular heat flux fields at the fiber center that are challenging for conventional approaches. In contrast, continuity and periodicity conditions are enforced via boundary collocation points in the loss function. Validation against finite element simulations across a wide range of fiber volume fractions shows that accurate and converged temperature distributions can be achieved after 9000 training epochs using 8-16 harmonic terms. Additional higher-order harmonics are difficult to train reliably and may degrade predictions. While strong agreement is observed in the matrix heat flux distributions, noticeable discrepancies persist in the fiber phase due to varying ability to capture the singular heat flux fields. Furthermore, uniform collocation points converge faster than random points during solution refinement. Finally, transfer learning is employed to accelerate training for new configurations, allowing the network to achieve comparable accuracy after only 2000 training epochs, which is substantially fewer than the 9,000 epochs required when training from scratch.

In this brief review, multiscale modeling of dissipative composites undergoing fully coupled thermomechanical processes is outlined through models presented in a collection of recent works. The aim is to demonstrate the challenges and limitations of: (1) multiscale approaches (full-field or mean-field techniques), (2) computational approaches dealing with complex material systems, (3) alternative methodologies dedicated to the analysis of composite structures, such as those based on data-driven modeling and model order reduction techniques.

In this study, a ZrO2 thin film was deposited onto a Ti6Al4V substrate using the Oblique Angle Deposition (OAD) technique. The influence of the substrate/Zr target angle (15°, 30°, 45°, and 60°) was investigated, with a fixed azimuthal orientation (Phi) of 180°. The primary objective of this work is to develop and characterize novel biocompatible coatings for hip prosthesis implants with a complex 3D spherical geometry. The OAD method enables thin film deposition on such geometries and enhances understanding of how the particle incidence angle affects the surface morphology and microstructure of zirconium oxide (ZrO₂) thin films. This study combines an experimental approach (DC magnetron sputtering) with a multi-scale numerical approach using Monte Carlo codes (SRIM, SIMTRA, and NASCAM). The structure, texture, and growth of the ZrO2 coatings were analyzed via X-ray diffraction (XRD), while microstructure and surface morphology were examined using scanning electron microscopy (SEM). Hardness and Young's modulus were determined through nanoindentation testing. Results indicate that increasing the oblique angle leads to a decrease in hardness. Experimental and numerical findings complement each other, offering deeper insight into the deposition phenomena. SIMTRA simulations closely replicate experimental observations: a higher number of incident particles results in increased coating thickness. Additionally, the film thickness decreases with increasing substrate inclination angle. The microstructure of ZrO₂ thin films is strongly influenced by substrate orientation, and coated substrates demonstrate superior performance compared to their uncoated counterparts.

Introduction Adolescent Idiopathic Scoliosis (AIS) is classically evaluated through static X-rays and health-related quality of life questionnaires that do not reflect the functional limitations of patients during daily life activities, such as walking. The aim was to investigate kinematic strategies in non-operated AIS with different types of curvature during walking using 3D gait analysis. Methods 13 AIS with Lenke 5 (major Cobb: 23 ± 8°), 30 AIS with Lenke 1 (major Cobb: 40 ± 14°) in addition to 24 controls underwent biplanar X-rays followed by 3D gait analysis. The kinematic parameters of the head, trunk, spinal segments, pelvis and lower limbs were compared between groups. Results AIS Lenke 5 had a lumbar segment bending while walking (T12L3-L3L5: 5 ± 7° vs. -3 ± 7° in controls) to the concave side of the scoliosis. They walked with an increased pelvic frontal mobility (12 ± 3° vs. 9 ± 3°) and internal rotation of the right foot (-2 ± 6° vs. -11 ± 8°; all p < 0.05). AIS Lenke 1 increased their thoracic & lumbar segment bending to the concave and to the opposite side respectively (T6T9-T9T12: -4 ± 9° vs. 1 ± 4°; T12L3-L3L5: 8 ± 12° vs. -2 ± 7°). However, they tended to reduce their lumbo-pelvic mobility (7 ± 5° vs. 12 ± 5°; all p < 0.05). Conclusion In response to their inherent lumbar stiffness and bending, AIS Lenke 5 patients tended to increase their pelvic frontal mobility and to develop a homolateral internal foot rotation, ensuring a dynamic alignment during gait. AIS Lenke 1, by producing opposite bending movement at the thoracic and lumbar segments, tended to reduce their lumbo-pelvic mobility and ensure coronal dynamic alignment.

This paper investigates the influence of substrate grain size on the behavior of a multilayer CrN/CrAlN coating, with the bilayer thickness varying across the cross-section in the range of 200–1000 nm. The substrate tools were made of WC-Co sintered carbide with three different grain sizes. The coatings were subjected to mechanical and tribological tests to assess their performance, including nanohardness, scratch resistance, and tribological testing. The coating's roughness was measured using a 2D profilometer. Additionally, the chemical composition and surface morphology were analyzed using Scanning Electron Microscopy (SEM) and Energy Dispersive X-Ray Spectroscopy (EDX). The durability tests were performed on an industrial CNC machine tool on particleboard. The results revealed that tools with ultra-fine nano-grain (S) and micro-grain (T) WC-Co substrates exhibited a significant increase in tool durability by 28% and 44%, respectively. Significant differences in the microgeometry of the substrate U, especially in relation to the tool based on substrate S, explain the lack of improvement in its durability despite the use of a multilayer coating.

This study investigates the influence of void shape and orientation on the Forming Limit Diagrams (FLDs) of porous materials with non-quadratic anisotropy. The constitutive framework integrates the Gologanu–Leblond–Devaux (GLD) damage model, which accounts for void morphology, with Barlat’s YLD-2004-18p non-quadratic yield criterion to capture metal matrix plastic anisotropy. The combined GLD-YLD model is further coupled with the Marciniak–Kuczyński (M–K) imperfection approach to predict FLDs for anisotropic sheet metals. Results demonstrate that void morphology considerably affects formability, with prolate (needle-like) voids enhancing material ductility, as compared to oblate (plate-like) voids, while spherical voids yield an intermediate behavior. Furthermore, the study highlights that the impact of material orientation on formability involves a complex interplay of several factors, which include coupled matrix-induced and void-shape-induced anisotropy, the relative angle between the rolling direction and void orientation, and void nucleation mechanism. The model predictive capabilities are assessed against experimental FLD data for two aluminum alloys. Although these alloys show only slight sensitivity to void morphology, due to low porosity, the void shape-dependent anisotropic GLD-YLD model better captures the experimental trends as compared to the undamaged isotropic von Mises model, which overly overestimates formability on the right-hand side of FLD. The role of isotropic hardening is also examined, which shows that higher hardening improves formability, and the effect is smallest for oblate voids under balanced biaxial loading. These findings underscore the importance of incorporating both damage and matrix-induced anisotropy in constitutive modeling for accurate FLD prediction.