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3D printing strategies and mechanical performance assessment of continuous basalt-fibre/PA12 composites with high fibre volume fraction
No full textInternational audienceThe continuous filament fabrication (CFF), based on the fused filament fabrication (FFF), is a technology enabling to produce 3D-printed composite parts with high mechanical performance. Basalt fibres remain rather unused in CFF among fibrous reinforcements, in spite of properties similar to glass fibres. In addition, in the context of in-situ resource utilization for out-of-earth manufacturing, basalt fibres stand as an excellent earth-based analogue material of lunar regolith.This study aims at bringing deeper insights on the effect of specific printing strategies, by tuning the slicing parameters and by using a customized printer for CFF, as well as on the thermo-elastic properties of a novel high-performance composite material consisting of continuous basalt fibres embedded into a PA12 matrix, with a fibre volume fraction of 55 %.Thermal and morphological properties of the filament are first characterized. Then, the optimal slicing parameters – layer height (LH) and interfilament distance (ID) – are explored through the prism of process-induced porosity; a pore volume content as low as 2.15 % is reported for LH = 0.2 mm and ID = 1.15 mm, testifying to the high material quality and process handling. Finally, the in-plane mechanical properties of PA12-basalt are measured in the quasi-static regime, including longitudinal (0°) tension and compression, transverse (90°) tension, in-plane shear (±45°) and out-of-plane three-point bending. An extreme anisotropic behaviour is revealed. Competitive mechanical properties in the fibre direction are reported, comparable to similar glass-fibre reinforced composites. Conversely, those in the transverse direction are relatively moderate. The latter are attributed to the localized porosity induced by the juxtaposition of adjacent filaments for the interfilament distance selected
Exploring the usability and creativity enhancement of augmented reality in additive manufacturing-based product design
No full textInternational audienceAugmented Reality (AR), a technology that overlays digital content onto the physical environment, holds promise for enhancing creativity and usability in product design education. However, despite the advantages of Additive Manufacturing (AM) in enabling complex and customizable designs, designers often struggle to grasp its abstract principles. Grounded in theories of immersive learning and multimodal visualization, this study investigates whether integrating AR visualization can facilitate better understanding and stimulate creativity in AM education. A controlled experiment was conducted with 34 master's students in product design, randomly assigned to either an AR-based learning group or a traditional card-based learning group. Participants engaged with AM principles through either an interactive AR application featuring manipulable 3D cube models or static information cards. Usability perceptions and creativity of design outputs were assessed respectively through structured questionnaires and expert evaluations by five domain specialists. Mann–Whitney U tests, appropriate for non-normally distributed data, revealed that the AR group reported significantly higher usability ratings and produced more original design outcomes compared to the card-based group. These findings demonstrate that AR-based educational tools can directly improve the usability and creative engagement of students in learning AM principles. This study contributes to advancing the understanding of how immersive technologies can be effectively in
Évaluation des Paramètres de Modélisation par Éléments Finis dans le Contexte du Formage Incrémental Robotisé
No full textInternational audienceSingle Point Incremental Forming (SPIF) is a flexible, cost-effective solution for manufacturing complexsheet metal parts in small batches. The accurate prediction of forming forces is a key challenge forprocess optimization and understanding material behavior. This study proposes a finite element (FE)modeling methodology aimed at reproducing the experimentally measured axial forces exerted by thetool. An independent parametric analysis was conducted on four critical modeling parameters: yieldcriterion, hardening law, type of analysis (isothermal or thermomechanical), and mesh refinement. Theintegrated configuration combines the options that individually minimized the overestimation ofsimulated forces. Results show a reduction in the mean relative error from 25% to 12%, withoutincreasing computational cost.Le formage incrémental est une solution flexible et économique pour la production de pièces complexesen petite série. La prédiction fidèle des forces de formage constitue un enjeu central pour l’optimisationdu procédé et la maîtrise du comportement matériau. Ce travail propose une méthodologie demodélisation par éléments finis (EF) visant à reproduire les forces expérimentales mesurées dans l’axede l’outil. Une analyse paramétrique indépendante est menée sur quatre paramètres clés : critère deplasticité, loi d’écrouissage, type d’analyse (isotherme ou thermomécanique) et raffinement dumaillage. La configuration intégrée combine les choix ayant, individuellement, minimisé lasurestimation des efforts simulés. Les résultats montrent une réduction de l’erreur relative moyenne de25 % à 12 %, sans surcoût numérique
Ontology-driven LLM framework for knowledge graph in smart buildings
No full textInternational audienceThe rapid data growth in smart building environments requires advanced tools to integrate, interpret, and utilize this information effectively. Smart buildings generate vast and heterogeneous data streams, including sensor readings, occupancy metrics, and environmental conditions, which are critical for optimizing energy efficiency, enhancing occupant comfort, and enabling predictive maintenance. However, the lack of a structured approach to automatically connect and contextualize these data sources limits the insights that can be derived. To address these challenges, this paper presents a framework for assessing data in a knowledge graph by automatically retrieving entities and establishing relationships from diverse data sources, incorporating metadata standards and time-series data relevant to smart buildings. A standard ontology from the domain is used to drive the experiment, enabling the automatic construction of a semantic graph-based model for a real-world smart building environment. The framework s objective is to ensure information is comprehensible as a preliminary step to intelligent decision-making in data-driven smart buildings, enabling applications like fault detection, performance measurement, and energy auditing. The proposed approach explores the potential of large language models (LLMs) to automate data integration, reducing reliance on experts. This paper addresses existing literature gaps on metadata mapping and lays the groundwork for future advancements in digital twin technologies for smart building applications
Multi-objective optimization of nanogrids for remote telecom base stations in Canada
No full textInternational audienceThe telecommunications sector targets net-zero emissions by 2050, yet many remote Canadian base stations rely on diesel generators, incurring high costs and emissions. Most hybrid renewable energy system (HRES) studies overlook snow accumulation, limiting relevance in northern climates. This work proposes a snow-aware hybrid nanogrid for a telecom base station in Dorval Lodge, Quebec, using bifacial PV modules, lithium iron phosphate (LFP) batteries, and a diesel generator. A preliminary HOMER Pro study showed 99% renewable penetration is technically possible but at high cost and without snow, bifacial, or aging effects. We developed a high-fidelity model including hourly snow coverage, seasonal albedo, battery aging, and diesel fuel emission behavior. A joint multi-objective optimization minimizing life cycle cost (LCC) and annual CO 2 under LP SP < 0.0001% was solved using a Controlled Elitist NSGA-II algorithm. Three stages were tested: baseline, fixed controls, and monthly adaptive controls. The adaptive strategy achieved the largest gains, cutting CO 2 by 18.59% and LCC by 5.26% versus baseline, with the most sustainable setup using 856 L/year (2.93 t CO 2 ). Sensitivity analysis showed snow-aware designs avoid up to 40.9% higher LCC and 139.7% more CO 2 seen in snowunaware cases. Integrating climate-specific snow modeling with adaptive controls enhances economic and environmental performance, offering a robust, transferable solution for remote telecom power in harsh climates.</div
Analysis of brake squeal using the Harmonic Balance Vector (HBV) signal model: dependence of squeal frequency, fundamental and harmonic shapes on operational parameters
No full textInternational audienceAnalysis of brake squeal using the Harmonic Balance Vector (HBV) signal model: dependence of squeal frequency, fundamental and harmonic shapes on operational parameter
LEAN et outils d'immersion à l'heure de l'Industrie 5.0: Une coexistence encore à justifier
No full textInternational audienceIndustry 5.0 (I5.0), promoted by the European Commission, represents an evolution of Industry 4.0 (I4.0) byemphasizing technology integration with humans. However, most scientific studies to date focus primarily on technicalaspects, neglecting human and social impacts. LEAN, which values continuous improvement and respect for employees,could play a key role in adopting immersive technologies such as Digital Twins (DT) and Virtual Reality (VR). In France,the JENII program aims to develop immersive learning environments to train future professionals, particularly in LEANprinciples. However, the literature review presented here reveals that the real impact of these technologies on operatorsremains poorly documented, and their compatibility with LEAN fundamentals remains uncertain. This research, therefore,highlights a lack of empirical data and calls for more experiments in real-world environments, particularly through learningfactories. Future studies could fill significant gaps by exploring the physical, psychological, and cognitive impacts ofimmersive technologies on operators. These advancements offer opportunities to enrich educational approaches and supportthe deployment of I5.0 technologies in companies in line with LEAN principles.L'Industrie 5.0 (I5.0), promue par la Commission Européenne, marque une évolution de l'Industrie 4.0 (I4.0) en mettant l'accent sur l'intégration des technologies avec l'humain. Toutefois, la majorité des études scientifiques se concentrent à ce jour sur les aspects techniques, négligeant les impacts humains et sociaux. Le LEAN, qui valorise l'amélioration continue et le respect des employés, pourrait jouer un rôle clé dans l'adoption des pratiques de l'industrie 4.0, dont les technologies immersives comme les Jumeaux Numériques (JN) et la Réalité Virtuelle (RV). En France, le programme JENII vise d'ailleurs à développer des environnements d'apprentissage immersifs pour former les futurs professionnels, notamment aux principes du LEAN. Cependant, la revue de littérature proposée ici révèle que l'impact réel de ces technologies sur les opérateurs reste peu documenté et leur compatibilité avec les fondamentaux du LEAN demeure incertaine. Cette recherche met donc en évidence un manque de données empiriques et appelle à davantage d'expérimentations en environnements réels, notamment via les usines-écoles. Ces études futures pourront ainsi combler d'importants manques en explorant les impacts physiques, psychologiques ou cognitifs des technologies immersives sur les opérateurs. Ces avancées offrent des opportunités pour enrichir les approches pédagogiques et soutenir le déploiement des technologies d'I5.0 en entreprise, en accord avec les principes du LEAN
Analyse du cycle de vie du stator d'une machine électrique de forte puissance
No full textNational audienceThis article presents a comprehensive Life Cycle Assessment (LCA) of the stator of a high‑power (6.5 MW) electrical machine, carried out using real industrial data and modelled in Brightway2 with the Ecoinvent 3.8 database and the Environmental Footprint 3.1 method. The inventory encompasses the manufacturing, use (continuous S1 service over 30 years) and end‑of‑life phases, with several comparative scenarios (intermittent loading, European electricity mix, loss reduction measures, and incorporation of recycled materials following two different approaches). The results show that the use phase dominates the environmental footprint—particularly for climate change, non‑renewable resource depletion, and human toxicity. Manufacturing impacts are more moderate but still significant for material resource consumption, whereas the end‑of‑life stage emerges as the most promising lever to reduce demand for virgin raw materials, especially when a high recycled content is assumed. Finally, the study identifies two priority eco‑design avenues: (1) enhancing energy efficiency via low‑loss magnetic laminations, and (2) optimizing end‑of‑life recycling loops. These strategies, coupled with finer modelling of industrial processes and recovery scenarios, open the way to more sustainable electrical machines from the very design phase.RESUME-Cet article présente une étude complète de l'Analyse du Cycle de Vie (ACV) du stator d'une machine électrique de forte puissance (6,5 MW), réalisée à partir de données industrielles réelles et modélisée sous Brightway2 avec la base Ecoinvent 3.8 et la méthode Environmental Footprint 3.1. L'inventaire couvre les phases de fabrication, d'usage (service continu S1 sur 30 ans) et de fin de vie, avec plusieurs scénarios comparatifs (charge intermittente, mix électrique européen, réduction des pertes, incorporation de matériaux recyclés selon deux méthodologies). Les résultats révèlent que la phase d'usage domine l'empreinte environnementale, en particulier pour le changement climatique, l'épuisement des ressources non renouvelables et la toxicité humaine. Les impacts de la fabrication sont plus modérés mais restent significatifs pour les ressources matérielles, tandis que la fin de vie apparaît comme le levier le plus prometteur pour réduire les besoins en matières premières vierges, surtout lorsque l'on intègre un contenu recyclé élevé. Enfin, l'étude met en lumière deux axes prioritaires d'éco-conception : (1) l'amélioration du rendement énergétique via l'utilisation de tôles magnétiques à faibles pertes, et (2) l'optimisation des boucles de recyclage en fin de vie. Ces pistes, associées à une modélisation plus fine des procédés industriels et des scénarios de valorisation, ouvrent la voie à des machines électriques plus durables, dès leur phase de conception.</div
Unleashing the Potential of Magnesium Nanoparticles: A Green Synthesis for Sustainable Sensing Solutions
No full textInternational audienceFor the first time, we unveil an innovative, one-step approach for synthesizing magnesiumnanoparticles (MgNPs), combining efficiency, simplicity, and environmental sustainability.Our method utilizes spin-coating of a magnesium precursor-loaded poly(methyl methacrylate)(PMMA) dispersion onto n-doped silicon substrates. This process induces vapor-driven phaseseparation, leading to the self-assembly of PMMA into a nanoporous film that encapsulatesMgNPs within its nanoholes. Unlike traditional methods that depend on toxic reducing agentsor complex synthesis routes, our approach eliminates the need for additional stabilizers, makingit a greener and more efficient alternative for nanostructure fabrication. By systematicallyoptimizing precursor concentration and spin-coating speed, we precisely control the size anddispersity of MgNPs, achieving spherical nanoparticles of approximately 50 nm − the smallestsize reported to date. Optical characterization using micro-extinction spectroscopy confirms thepresence of localized surface plasmon resonance (LSPR) in the Mg/PMMA composite,demonstrating its potential for advanced sensing applications. X-ray photoelectronspectroscopy (XPS) reveals that MgNPs are passivated by a native MgO layer, enhancingstability and minimizing oxidation under ambient conditions. These substrates exhibitoutstanding surface-enhanced Raman scattering (SERS) sensitivity, detecting 4,4′-Bipyridine(4,4’-BP) at low concentrations. This innovative strategy offers a sustainable path fordeveloping earth abundant plasmonic nanomaterials with tunable optical properties
Fluid-structure modeling of iceberg capsize
No full textInternational audienceIce sheet evolution models require consideration of ocean-glacier processes such as iceberg calving. The capsize of large icebergs from marine-terminating glaciers as a result of calving generates teleseismic waves that contain information about the physical processes involved. Deriving the calved iceberg volume from the seismic signal requires the coupling of seismic inversion with numerical simulation of the capsize, which currently lacks high-fidelity hydrodynamic effects. Therefore, a Computational Fluid Dynamics model combining a Reynolds Averaged Navier-Stokes Equations solver with a volume penalization method and a spring-damper contact force formulation is used to simulate the capsize of an iceberg colliding with a glacier front. The modeled capsizes are in good agreement with laboratory-scale experimental results from the literature, for many configurations (capsize in the open ocean or in contact with a floating or grounded glacier terminus, for bottom-out or top-out configurations and several iceberg aspect ratios). In grounded glacier configurations, we show that the lateral flow confinement effects are significant. The initial iceberg tilt angle and the iceberg/glacier friction coefficient have little influence. Turbulence mainly affects the post-capsize iceberg drift velocity. In addition, a field-scale 200 m high iceberg capsize simulation shows near-field water velocities in excess of 10 m/s