Laboratoire Roberval

Multi-walled carbon nanotubes (MWCNTs) and graphene nanoplatelets (GNPs) were embedded into poly(vinylidene fluoride) (PVDF) electrospun fibers with varying mass fractions (0–4 wt%). In addition, the hybrid combinations at 1.5 wt.% GNP/MWCNT/PVDF were fabricated to evaluate potential synergistic effects. The electrospun fiber mats were thermally post-treated at 145°C for 3 and 6 h to assess the influence of annealing on the microstructure, crystalline phase composition, and functional performance. The morphological, structural, mechanical, and piezoelectric properties were examined in relation to both the distinct geometries of the fillers and the thermal annealing. The combination of annealing and nanofiller-induced nucleation significantly improved the crystallinity of the fibers. The tensile strength increased up to 11.1 MPa for 0.5 wt.% MWCNT/PVDF and 15.7 MPa for 1.5 wt.% GNP/PVDF after 6 h of annealing. Structural analysis revealed a pronounced α→β phase transition in the 1.5 wt. % hybrid compositions, reaching a β/α ratio up to 7.8 substantially greater than the transitions observed for mono-filler systems: 4.7 for 1.5 wt.% GNP/PVDF and 3.0 for 1.5 wt.% MWCNT/PVDF. The effect of the high β-phase content was further confirmed at the nanoscale by piezoresponse force microscopy (PFM), which showed a consistent piezoelectric response through the fibers: a coercive voltage of approximately ± 40 V, which decreased to ± 20 V, ascribed to the dispersed nanofillers within the fiber. These results demonstrate that combining hybrid carbon fillers with controlled annealing enables tunable crystalline structures and enhanced electromechanical performance in electrospun PVDF nanofibers. • hybrid GNP/MWCNT fillers drive synergetically β-phase nucleation mechanisms. • The interfacial interaction between PVDF and carbon fillers was quantified using Piezoresponse Force Microscopy (PFM) measurements. • PFM confirmed a consistent piezoelectric response with coercive voltage decreasing from ± 40 V to ± 20 V due to dispersed nanofillers • Hybrid fillers and annealing tailor PVDF crystallinity and functional performance.

Prediction of fatigue failure in concrete structures remains a major challenge due to progressive material degradation. Reliable prediction, therefore, requires modeling the 3D heterogeneous microstructure of concrete to explain the underlying mechanisms governing fatigue failure. While such mesoscale models can reliably predict the fatigue-induced fracture mechanisms, the identification of the associated material parameters remains a significant challenge due to the high-dimensional parameter space introduced by the model. The key challenge addressed in this study is to capture microcrack initiation and coalescence under fatigue loading, using a model capable of representing fracture process: crack initiation, crack propagation, and final failure. Firstly, concrete domain is discretized into Voronoi cells, enabling explicit representation of aggregates and mortar by randomly assigning cohesive links connecting Voronoi cells as aggregates and mortar. After this, mortar links are modeled as coupled damage–plasticity 3D Timoshenko beam elements with nonlinear kinematic hardening and isotropic softening introduced using embedded discontinuity formulation, enabling fracture Modes I–III, whereas aggregate links are modeled as elastic 3D Timoshenko beam elements. The model efficiency is additionally reinforced by using surrogate model approach, with corresponding material parameter identification carried out by multi-objective Bayesian optimization framework to reproduce experimental results. The performance of the proposed model is illustrated by reproducing experimental results obtained from concrete cube compression test and three-point bending test under low-cycle fatigue loading, where the errors between experimental and numerical results are reduced by 82% (stress) and 88% (energy) for the cube test and by 86% (force) and 93% (energy) for the bending test, relative to the initial dataset error.

High-pressure hydrogen storage vessels type IV (HPV), made from carbon fiber-reinforced composites, are essential for hydrogen-powered applications due to their strength and light weight. However, they remain vulnerable to internal defects such as porosities, which can compromise structural integrity. While certification tolerates some porosity, excessive levels may lead to failure. This study aims to predict both the number of porosity and the porosity rate category (low vs. medium/high) using XTRACTIS (XTS), a general reasoning artificial intelligence system, capable of automatically inducing robust and intelligible predictive models. For porosity rate classification, XTS and Boosted Tree were unable to find a robust model, due to a lack of information and erroneous values of the variable to be predicted. To model the log10 of the number of porosities, XTS automatically selected 15 out of the available 58 manufacturing variables to build 56 fuzzy IF…THEN rules, achieving an RMSE of 7.94% and a correlation of 0.824 on an external test dataset. The interpretable rules reveal relationships between process parameters and porosity characteristics, thus facilitating the understanding of defect formation mechanisms. The analysis revealed that uniform and stable fiber tension improves compaction and reduces voids, while heterogeneous or insufficient tension increases porosity. Optimized mandrel speed and winding angle enhance fiber alignment and resin distribution. Precise control of winding layer volume and doctor blade angle prevents dry zones and resin-rich areas, reducing porosity risk. Furthermore, the results emphasize the importance of monitoring and tuning multiple interacting parameters simultaneously to ensure composite quality. These findings highlight the value of augmented fuzzy cognitive AI in uncovering interpretable, domain-relevant interactions, supporting quality control and process optimization in advanced composite manufacturing.

In the scenario of digital transformation projects, user adoption remains a critical challenge, especially for complex software like product lifecycle management (PLM). Recently, digital adoption platforms (DAPs) have emerged, providing new opportunities to tackle this problem. However, many digital transformation projects still have a high failure rate due to the lack of optimal knowledge acquisition processes for users. This paper discussed the cause of this phenomenon and practiced different creative problem-solving approaches following the principle of design thinking. Accordingly, a heuristic guiding tool has been designed. This tool provides decision support for project managers in selecting the optimal digital adoption platforms according to their project context, fostering collaborations between stakeholders during the user adoption process. The proof of concept is carried out to validate the tool, using a PLM deployment project as support. The result confirms the tool's feasibility to support DAP selection in PLM deployment project.

This article presents the design and analysis of a two degrees of freedom microactuator, optically controlled by a laser source. The actuator consists of an array of permanent magnets and a diamagnetic levitating pyrolytic graphite sheet (PGS) that achieves discrete, stable, and frictionless motion above the array without the need for feedback control. The actuation mechanism relies on the temperature dependent susceptibility of the PGS, where laser induced heating creates a localized imbalance in levitation forces, enabling controlled movement. The stable levitation positions were analytically determined using the zero-torque minimum potential energy method, and the system response to laser power was validated through both simulations and experiments. Results demonstrate that the actuator achieves reliable motion with submicron positioning accuracy, an average velocity of 7 mm/s, a 5% settling time of 1.80 s, and an energy efficiency of 2.86 mm/J. This work demonstrates the potential of optical, wireless control for precise, and noncontact actuation in microscale systems, with applications in micromanufacturing and automated microassembly.

Magnetic localization techniques present some interests for micro robotics applications using smart surfaces due to their inherent advantages, such as integration ability or low cost. However, at microscale, these methods often encounter challenges to perform with high precision in real scenario, because of the limitation of calculation algorithms (numerical stability, convergence, approximation methods) and the impact of measurement uncertainties (misalignment, sensor performance, etc.). This paper presents a new 2D magnetic localization method based on a 3D Hall effect sensor array system, in order to localize a cubic magnet. The proposed approach combines an efficient numerical resolution algorithm (Newton-Raphson) with precise analytical models of the magnetic field of the magnet to brave these measurement uncertainties and to achieve high-resolution 2D magnetic localization. Through performance assessments from simulation calculations and experimentations, the operation and efficiency of the proposed method is demonstrated. Results indicate good performance of the Newton-Raphson method with micrometric accuracy, even when there are measurement uncertainties in real scenario. Experimental test realized with a prototype of 3D Hall effect sensor array confirms the efficiency of the 2D magnetic localization with an averaged 12.73 μm localization error. A comparative analysis shows that this new magnetic localization method is well placed in the hierarchy of existing methods, that estimate the 2D position of a magnet by using a Hall effect sensor array system.

When a resonator is added to an acoustic cavity or a vibrating structure, a new resonance frequency, or pole, is created in the system. When dissipation is present, this may breakdown when some parameters are finely tuned. This phenomenon, typical of non-Hermitian dynamics is called an exceptional point in the parametric space where two (or more) eigenvalues, as well as their associated eigenvectors, coalesce. In the recent years, EPs have gained interest in physics because of the counter-intuitive concepts associated with them. In the fields of acoustics and vibration, understanding EP may lead to a better comprehension of modal energy exchanges and decay. This work aims at exploring the key concepts related to EPs by revisiting the well-known coupled pendulums problem in the presence of damping. First, the free response of the experimental system is investigated after tuning it on an EP. Then an encircling is performed by varying the parameters through time. Our experimental results illustrate in a pedagogical manner nearly-optimal dissipation, energy exchange and chirality effects which have been recently evidenced in physics.