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Development and analysis of methodologies for accurate identification of a mechanistic cutting force model in milling - application to high-feed milling of Ti-6Al-4V titanium alloy
No full textInternational audienceAccurate prediction of cutting forces is essential for process planning and optimisation in modern machining. Mechanistic modelling is widely used for this purpose, but its accuracy is strongly affected by imprecise evaluation of uncut chip thickness and cutter-workpiece engagement. In addition, the choice of a suitable cutting force model and its identification methodology are non-trivial and directly influence cutting force prediction. This article introduces a general methodology for the modelling of uncut chip thickness in both conventional and high-feed milling. The approach is based on a parametric description of milling cutters combined with an algorithm for the computation of uncut chip thickness. This framework accounts for geometrical effects such as tool run-out, differential pitch and insert geometry, enabling a more reliable description of cutter-workpiece engagement than classical approximations. The methodology is coupled with mechanistic cutting force models and validated experimentally through instrumented high-feed milling tests of Ti-6Al-4V titanium alloy. An inverse identification strategy is developed to determine cutting force model coefficients from measured forces, including a quantitative correction of tool angular shift between simulation and measurement. In addition, the influence of the transition uncut chip thickness on the fractional model is investigated.Finally, the variability of identified coefficients is analysed across tool revolutions and cutting conditions. Sensitivity analysis demonstrates how this variability impacts simulated forces, providing a criterion for assessing the relevance and robustness of cutting force models. The proposed methodology thus offers a systematic framework for accurate force prediction and critical evaluation of mechanistic models in milling
Subsurface microstructure versus surface topography: Impact on the fatigue strength of stress-relieved Laser Powder Bed Fusion (L-PBF) 316L parts
No full textInternational audienceThe Laser Powder Bed Fusion (L-PBF) process allows to manufacture parts with both a complex geometry and a high mechanical performance. The as-built and net-shape L-PBF 316L stainless steel parts - i.e. without any heat nor surface treatment, have tensile residual stresses, subsurface pores, a rough surface and a contourcore microstructure that synergistically lead to a low fatigue strength. Residual stresses can be relieved with a heat-treatment to enhance the fatigue properties, but the best performance is obtained after machining and polishing. The differences between the polished and the heat-treated net-shape conditions lie in the subsurface microstructure, the surface topography, and the population of subsurface pores. This study aims to quantify the impact of each of these subsurface parameters on the fatigue behaviour. To do so, the subsurface microstructure of the net-shape condition is characterised. Then, uni-axial fatigue tests are carried out on stress-relieved specimens with the following surface conditions: net-shape, partially polished, and ‘‘machined and polished’’. The Kitagawa-Takahashi representation shows that for defects smaller than 200 μm, the subsurface microstructure is the most influential parameter on the fatigue strength. Conversely, the surface topography has a limited influence. After examining the microstructure surrounding killer defects of both netshape and polished specimens, the grain size under the surface of all surface conditions is considered in the Kitagawa-Takahashi diagram relatively to the killer defect size: this allows to align the various batches’ results
A Literature Review of Public Transport OD Matrix Estimation
No full textInternational audienceOrigin–Destination matrices (ODms) are a fundamental input for public transport planning and optimization, as they characterize travel demand across a network. Traditionally estimated from user surveys, ODms are now increasingly inferred from large-scale automatically collected data, such as Automated Fare Collection (AFC), Automated Passenger Counting (APC), and Automated Vehicle Location data (AVL). This review focuses on the reconstruction of static ODms in public transport systems, while accounting for studies that exploit dynamic or short-term observations when these are used to infer static or quasi-static demand patterns. We provide a transversal synthesis of OD estimation approaches by jointly analyzing data sources, modeling assumptions, uncertainty handling, and validation strategies. A structured comparative table summarizes representative case studies across different data contexts, objectives, and methodological families. Beyond a descriptive overview, this review identifies key research gaps, including the lack of uncertainty-aware benchmarking frameworks, the limited propagation of uncertainty across modeling stages, and the strong dependence of reported performance on data quality and validation references. These findings highlight that OD estimation performance is context-dependent and that methodological choices should be aligned with data availability, modeling objectives, and acceptable assumptions rather than with reported accuracy alone
Assessment of Jet Inflow Conditions on the Development of Supersonic Jet Flows AIP/123-QED Assessment of Jet Inflow Conditions on the Development of Supersonic Jet Flows
No full textInternational audienceAssessment of Jet Inflow Conditions on the Development of Supersonic Jet FlowsIn the present work, large-eddy simulations of free supersonic jet flows are performed to investigate the influence of inflow conditions on the jet flow field and its turbulent properties. A high-order nodal discontinuous Galerkin method is employed to solve the governing equations on the generated mesh. Three different inflow profiles are evaluated to represent the nozzle-exit conditions, namely, inviscid, steady viscous, and unsteady viscous profiles. Velocity and shear stress tensor component profiles obtained from the simulations are compared with experimental data. Among the investigated profiles, the steady viscous inflow shows the most significant deviation from the inviscid case, particularly in the near-field region of the jet inlet. The steady viscous profile also leads to reduced peak velocity fluctuations, showing better agreement with experimental results. Further downstream, the influence of the inflow condition diminishes, with all three profiles converging toward the experimental reference. In addition, power spectral density analyses of streamwise velocity fluctuations reveal that the inflow conditions have little effect on spectral distributions, with numerical results showing consistent agreement with experimental data within the accessible Strouhal range. Beyond these findings, the study provides a highly detailed, high-fidelity database of supersonic jet flow simulations, encompassing six largeeddy computations with different meshes, polynomial refinements, and inflow conditions.The database includes high-frequency data in relevant regions of the jet flow field and is openly available in the Zenodo repository, ensuring accessibility and reusability for the scientific community.</p
Imagerie par balayage pour la mécanique : Microscopie à Force Atomique (AFM) et Électronique à Balayage (MEB)
No full textNational audienceL’étude du comportement mécanique des matériaux nécessite aujourd’hui de descendre auxéchelles micro- et nanoscopiques pour identifier les mécanismes physiques à l’origine de ladéformation réversible ou irréversible et à la rupture. Ce cours propose une initiation à deuxtechniques d’imagerie par balayage répandues en science des matériaux : la MicroscopieÉlectronique à Balayage (MEB) [1,2] et la Microscopie à Force Atomique (AFM) [3,4]. Après unbref historique des techniques et de leur évolution, les principes de base de formation del'image (interaction sonde-matière, capteurs, balayage, etc.) seront introduits. Les dispositifsexpérimentaux actuels typiques avec leur précision/résolution et les temps d’obtention d’uneimage suivant les conditions expérimentales et les modes d'imagerie les plus classiques serontprésentés. Comme tout appareil de mesure, des artefacts peuvent subsister malgrél’optimisation des réglages. En se concentrant sur les artefacts pouvant générer desincertitudes de mesures mécaniques, nous verrons quels sont les paramètres à optimiser lorsde la formation de l’image pour minimiser les artefacts de mesure et/ou les correctionspossibles ainsi que les calibrations nécessaires. Le cours se terminera par un ensembled'exemples issus de la bibliographie pour lesquels un MEB et/ou un AFM est utilisé pourréaliser des images au cours d’essais mécaniques. Ces images servent ensuite à extraire desdonnées cinématiques via la corrélation d'images 2D voire 2,5D. Nous donnerons desexemples d’essais réalisés dans différents environnements, en température ou encore souschargement électromagnétique afin de mettre en évidence la diversité d’applicationsenvisageable. Pour finir, nous aborderons le cas de caractérisations plus « directes » ducomportement élastique par AFM ou l’utilisation de faisceau FIB pour miniaturiser les essaisin situ MEB. En conclusion, nous dresserons un bilan critique des performances respectives deces deux techniques en termes de résolution, de taille/dimensions et de vitesses d'acquisitionde l’image ainsi que des limites intrinsèques (matériaux non conducteurs, environnement, …)
Early-Reverberation Imaging Functions for Bounded Elastic Domains
No full textInternational audienceFor the ultrasonic inspection of bounded elastic structures, finite-duration imaging functions are derived in the Fourier-Laplace domain.The signals involved are exponentially windowed, so that early reflections are taken into account more strongly than later ones in the imaging methodology.Applying classical approaches to the general case of anisotropic elasticity, we express the Fréchet derivatives of the relevant data-misfit functional with respect to arbitrary perturbations of the mass density and stiffnesses in terms of forward and adjoint solutions.Their definitions incorporate the exponentially decaying weighting. The proposed finite-duration imaging functions are then defined on that basis.As some areas of the structure are less insonified than others, it is necessary to define normalized imaging functions to compensate for these variations.Our approach in particular aims to overcome the difficulty of dealing with bounded domains containing defects not located in direct line of sight from the transducers and measured signals of long duration.For this initiation work, we demonstate the potential of the proposed method on a two-dimensional test case featuring the imaging of mass and elastic stiffness variations in a region of a bounded isotropic medium that is not directly visible from the transducers
Micromechanics-informed neural networks for periodic homogenization of thermocondcutive behavior in unidirectional composites with cylindrically orthotropic graphite fibers
No full textInternational audienceA 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
The impact of mine ownership on trade of metal ores
No full textInternational audienceMetals are essential to the global economy, yet traditional criticality assessments, often based solely on the geographic concentration of mining production, overlook the corporate control dimension of risk. Here, we analyzed whether ownership structures affect trade patterns in critical minerals and examined how production and corporate control diverged from 2000 to 2022. We developed a comprehensive country-level dataset using S&P Capital IQ Pro for 12 key metals and metal ores, calculated Herfindahl-Hirschman indices (HHI) for production and ownership, and statistically tested the relationship between foreign mine ownership and international trade flows using logistic and fixed-effects regressions. Finally, we built scenarios for production and ownership in 2030 to match demand estimates from the International Energy Agency (IEA). Results showed only 2%-14% of global ore trade value overlaping with existing foreign ownership ties and no statistically significant relationship between foreign mine ownership and trade flows. Additionally, clear divergences emerged between ownership and production concentration: cobalt production was highly geographically concentrated (HHI of 4602 in 2022) but had dispersed corporate ownership (HHI of 1985), and high-income countries frequently held substantial ownership stakes despite declining shares of actual production. Projected scenarios indicated continued shifts, notably reduced cobalt and lithium production shares in traditional producer countries, offset by growing Canadian and Australian ownership. Although market dynamics do not appear to be influenced by ownership structures today, corporate control remains a potential lever for supply chain disruption. This underscores the need to incorporate ownership into criticality assessments for a more comprehensive understanding of supply risks.</div
Through my eyes: Integrative Model of Awareness-Raising (IMAR)
No full textInternational audienceThanks to the phenomena of presence and embodiment, immersive virtual reality makes it possible to create situations that influence users' mental representations. This can deepen understanding of another person's perspective and foster compassion and motivation for prosocial behavior. In the literature, several studies have shown positive correlations between presence in a virtual environment and empathy. Other studies have shown the influence of prosocial video games on increasing prosocial behavior in the real world. Numerous models have been developed to explain the complex mechanisms of empathy and prosocial behavior. Despite this abundance of research, existing models remain fragmented, each addressing a specific aspect of the complex mechanisms of empathy and prosocial behaviour. The literature does not yet provide a comprehensive framework for integrating these mechanisms in the specific context of awareness. Based on the knowledge that has been established, we propose an Integrative Model of Awareness-Raising (IMAR) based on these solid theoretical foundations, not to revalidate them, but to mobilise them in the design and evaluation of awareness-raising tools. We measured the main variables of the IMAR model using standardized questionnaires, before and after users were exposed to a visual impairment awareness-raising experience in a virtual environment. This enabled us to assess the model's effectiveness in predicting the effects of an awareness-raising activity. We hypothesized a positive influence of the use of the virtual awareness-raising tool on participants' cognitive empathy, affective empathy and prosocial responses. In the experiment carried out, 46 participants were exposed to the immersive visual impairment awareness-raising tool. All were engineering students, aged between 22 and 32. To measure their level of presence and their mental representations of disability, the participants answered a first questionnaire just before the experiment, a second just after the experiment and a third one a week later. These measures enabled us to study the changes induced by the virtual awareness-raising tool, as well as their maintenance over time. The results show that awarenessraising in the virtual environment led to an increase in prosocial behavior, as well as its maintenance over time. Participants were more willing to give their time to charity after the awareness-raising session than before. This effect is particularly achieved through enhanced cognitive empathy. Based on the model, the results suggest that the awareness-raising tool could be improved in terms of affective empathy, by working on the narrative, for example. The main contribution of this paper is to propose an integrative model of the awareness-raising process based on the literature, as well as an experiment showing how this model can be used to interpret the impact of an awareness-raising approach and to identify areas for improvement.</div
Surrogate-assisted optimization for decision-making in lithiumion battery manufacturing
No full textInternational audienceIn modern manufacturing systems, digital twins (DTs) have emerged as transformative enablers of smart, adaptive, and data-rich production. A digital twin continuously synchronizes real-time data from physical assets (e.g., machines, sensors, and operators) with a virtual counterpart that mirrors the dynamic behavior of the production system. Such capabilities are particularly crucial in lithium-ion battery production, a process characterized by multi-stage workflows, nonlinear process dependencies, and tight interrelations between quality, energy efficiency, and time. Within this context, digital twins provide a virtual environment for testing parameter adjustments, anticipating defects, and minimizing downtime and contributing directly to the Zero-Defect Manufacturing (ZDM) paradigm. This vision is also central to the European BATTwin project, which develops a multilevel digital twin platform to enhance sustainability and defect reduction in Li-ion battery gigafactories across Europe. The proposed surrogate-assisted optimization framework demonstrates how lightweight predictive models can significantly accelerate decision-making when embedded within a digital twin for Li-ion battery production. By combining high-fidelity simulation with fast surrogate prediction, the system enables adaptive and robust decision-making while reducing computational cost. Future work will focus on enabling online updates of the surrogate model, incorporating uncertainty quantification, and expanding the optimization to fully multi-objective and real-time settings