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Alpha-case promotes fatigue cracks initiation from the surface in heat treated Ti-6Al-4V fabricated by Laser Powder Bed Fusion
International audienceThis research investigates the effect of the formation of an oxygen-stabilised titanium alpha layer – called alpha-case at the surface – on the fatigue properties of Ti-6Al-4V (Ti64) alloy components produced by Laser Powder Bed Fusion (L-PBF). Three post processing heat treatments with different controlled atmospheres were carried out on samples with as-built surfaces to evaluate how differences in alpha-case layer thickness and hardness affect the material’s susceptibility to surface embrittlement and its overall fatigue performance. The investigation includes bulk and subsurface microstructural analysis, surface characterisation by X-ray computed tomography (XCT), and fatigue testing. Key findings show that alpha-case layers can reduce the fatigue resistance of L-PBF fabricated Ti64. The presence of a 70±3 µm thick alpha-case layer was found to promote crack initiation. This is emphasised by a higher density of initiated cracks, thus leading to a reduction in fatigue life. Conversely, thinner alpha-case layers were found to have a reduced impact on the fatigue performance, highlighting the critical role of post processing heat treatments in modulating the fatigue resistance of the material. The use of XCT to characterise the surfaces of the specimens in 3D confirms that fatigue cracks primarily initiate at surface notches, highlighting the predominance of as-built surfaces over microstructure in determining the fatigue resistance of L-PBF Ti64 components
Room temperature electron beam sensitive viscoplastic response of ultra-ductile amorphous olivine films
International audienceThe mechanical properties of amorphous olivine (a-olivine) deformed at room temperature are investigated in situ in a TEM under uniaxial tension using a Push-to-Pull (PTP) device. Thin films of a-olivine were produced by pulsed laser deposition (PLD). With or without electron irradiation, a-olivine films deform plastically, with a gradual transition that makes impossible the determination of a precise threshold. The strength attains values up to 2.5 GPa. The increasing strain-rate in load control results in an apparent softening with stress drop. The fracture strain reaches values close to 30 % without e-beam irradiation. Under electron illumination at 200 kV, the strength is lower, around 1.7 GPa, while higher elongations close to 36 % are obtained. Alternating beam-off and beam-on sequences lead to exceptionally large fracture strains equal to 68 % at 200 kV and 139 % at 80 kV. EELS measurements were performed to characterize the interaction between the electron beam and a-olivine. At a voltage of 80 kV, radiolysis accompanied by oxygen release dominates whereas at high voltage (300 kV) the interaction is dominated by knock-on type defects. Radiolysis is also the main interaction mechanism at 200 kV with low exposition which corresponds to most of our in situ TEM deformation experiments. To interpret the mechanical data, a simple 1D model has been developed to rationalize the load transfer between the PTP device and the specimen. The strain-rate sensitivity is 6 to 10 times higher when a-olivine is deformed under electron irradiation
Subsurface hardening of Al irradiated with ultrafast infrared laser
This work has been funded by a public grant from the French National Research Agency (ANR) under the “France 2030” investment plan, which has the reference EUR MANUTECH SLEIGHT - ANR-17-EURE-0026.International audienceThe effect of femtosecond laser shock peening on a model Al-0.3Mn alloy was investigated experimentally and numerically by molecular dynamics. Micro-diffraction experiments performed at synchrotron source revealed the depth profiles of the residual stress and the stored energy of dislocations, a measure of local plasticity. The depth of the maximum compressive stress did not coincide with that of the maximum dislocation energy, which was found at the surface. The interaction between the laser and the metal was simulated with LAMMPS using a two-temperature molecular dynamics package. The model accurately described the equation of state of aluminum and showed nearly equal resolved shear stresses on all slip systems at the wavefront. The dislocation density at a depth of 1 μm, predicted by the Meyers' model [1], was higher than the experimental data, suggesting possible recovery due to the increased temperature of the sample after repeated shock loading
La formation des professionnels de santé par la simulation : quelles perspectives de recherche en sciences de gestion ? Réflexions à partir de deux études de cas
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A stochastic model based on Gaussian random fields to characterize the morphology of granular objects
International audienceThe geometrical modeling of granular objects is a complex challenge that exists in many scientific fields, such as the modeling of granular materials or rocks and coarse aggregates with applications in civil, mechanical, and chemical engineering. In this paper, a model called SPHERE (Stochastic Process for Highly Effective Radial Expansion) is proposed, which is based on the deformation of an ellipsoid mesh using multiple 3D Gaussian random fields. The model is designed to be flexible (full control over 2D and 3D morphological properties of granular objects), ultra-fast (over 1000 aggregates in less than 5 s), and independent of the mesh and base shape used (as long as it is a star-shaped object). The flexibility of the model and its ability to reflect real data is illustrated using images of latex nanoparticle aggregates. Using 2D measurements on images from a morphogranulometer, a method based on the SPHERE model is proposed to estimate the 3D morphological properties of aggregates. A multiscale optimization process is applied, in particular using a partial reconstruction of 2D shapes from elliptic Fourier descriptors, in order to best reproduce the shape, angularity and texture of the aggregates using the SPHERE model. Validation of the method on 3D printed data shows relative errors of less than 3% for all measured 2D and 3D morphological characteristics, and validation on a population of synthetic objects shows relative errors of less than 6%. The results are compared and discussed with those obtained using other models based on overlapping spheres and show consistency with previous work. Finally, suggestions for improvement are given
Scenario-based Optimization to Address Ergonomic Challenges in Assembly Line Balancing
International audienceAssembly lines are key manufacturing systems, but they often face issues like operation time variability or ergonomic risks for workers. These challenges lead to operational inefficiencies, including a rise in musculoskeletal disorders (MSDs), and increased economic costs due to absenteeism and worker compensation. This work aims to help make the workplace more efficient and cost-effective by improving worker conditions. Moreover, in addition to the operational difficulties involved, the variability also complicates the accurate assessment of a worker's physical strain.We introduce a new strategy for scenario-based optimization of the assembly line balancing problem, modeling ergonomics with a fatigue and recovery criterion. Here, operation times can vary according to different scenarios, each associated with a probability of occurrence. Our objective is to minimize the fatigue level of workers for a given percentage of scenarios. To achieve this goal, we propose an Integer Linear Programming (ILP) approach that prioritizes worker health under fixed economic conditions. An Iterative Dichotomic Search algorithm is used to solve the problem, and a numerical example is presented.Our method is a new approach that uses variable operation times to better align with real-world assembly line uncertainties. To the best of our knowledge, our model is the first to deal with ergonomics in cases with variable operation times for manual workers, thus enhancing its practical applicability and effectiveness in real-world industrial settings. Practitioners benefit from our model’s adaptability to operation variations, offering practical solutions for decision-making and enhanced operational management
Cybersécurité. Qui fait quoi ? Comment se protéger ?
National audienceL'Université pour Tous d'Aurec-sur-Loire organisait une conférence sur le thème : "Cybersécurité : comment se protéger ?"A cette occasion, Guillaume Muller, enseignant chercheur à l'École des Mines de Saint-Étienne, et Philippe Beaune, ancien enseignant chercheur de l'École des Mines de Saint-Étienne, ont donné une conférence sur la Cyber-Sécurité
Anomaly Detection in a Production Line: Statistical Learning Approach and Industrial Application
International audienceThis paper explores industrial engineering, particularly focusing on discrete processes and emphasizing real-time control of Production Lines within these processes. A critical component of this control involves the incorporation of a dashboard system, essential for providing workshop managers with valuable insights into the estimated time required for each Production Order (PO) to progress through the remaining stations in the production line.The key contribution of this work is the conception and development of a novel mathematical model, applied to real-world industrial data, capable of detecting anomalies within the production line. These anomalies are defined as deviations from expected timeframes. Constructed using Statistical Learning techniques and Information Theory, the model can be integrated within the dashboard framework, offering prompt identification of anomalies and ensuring optimal performance and efficiency of the production process
Design and Assessment of an Austenitic Stainless Alloy for Laser Powder Bed Additive Manufacturing
International audienceRecent developments in metallic additive manufacturing (AM) processes for the production of high-performance industrial pieces have been hampered by the limited availability of reliably processable or printable alloys. To date, most of the alloys used in AM are commercial grades that have been previously optimized for different manufacturing techniques. This study aims to design new alloys specifically tailored for AM processes, to minimize defects in the final products and to optimize their properties. A computational approach is proposed to design novel and optimized austenitic alloy compositions. This method integrates a suite of predictive tools, including machine learning, calculation of phase diagrams (CALPHAD) and physical models, all piloted by a multiobjective genetic algorithm. Within this framework, several material-dependent criteria are examined and their impact on properties and on the occurrence of defects is identified. To validate our approach, experimental tests are performed on a selected alloy composition: powder is produced by gas atomization and samples are fabricated by laser powder bed fusion. The microstructure and mechanical properties of the alloys are evaluated and its printability is compared with a commercial 316L stainless steel taken as a reference. The optimized alloy performs similarly to 316L in terms of coefficient of thermal expansion, hardness and elongation, but has a 17% lower yield strength and ultimate tensile strength (UTS), indicating that further optimization is required
Utiliser un Modèle de Performance pour Implémenter un CVA6 Superscalaire
A performance model of CVA6 RISC-V processor is built to evaluate performance related modifications before implementing them in RTL. Its accuracy is 99.2% on CoreMark. This model is used to evaluate a superscalar feature for CVA6. During design phase, the model helped detecting and fixing performance bugs. The superscalar feature resulted in a CVA6 performance improvement of 40% on CoreMark.Un modèle de performance du processeur RISC-V CVA6 est construit pour évaluer des modifications liées à la performance avant des les implémenter dans le RTL. Sa précision est de 99.2% sur le CoreMark. Ce modèle est utilisé pour évaluer une fonctionnalité superscalaire pour le CVA6. Durant la phase de conception, le modèle a aidé à détecter et résoudre des bogues de performance. La fonctionnalité superscalaire a résulté en une amélioration de la performance du CVA6 de 40% sur le CoreMark