6558 research outputs found
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Implicit Learning of Professional Skills through Immersive Virtual Reality: a Media Comparison Study
Full text linkThis study investigates the effectiveness of Immersive Virtual Reality (IVR) compared to traditional slideshow lessons in teaching implicit knowledge. For this purpose, the research focuses on professional decision-making skills in viticulture. Most existing research on immersive learning concentrates on explicit learning strategies. In contrast, this study explores the potential of IVR to foster the transfer of implicit knowledge to real-world situations.Forty third-year engineering students were randomly assigned to an IVR or a traditional slideshow group. They learned to assess vine vigour through an implicit learning phase, followed by a real-world evaluation in an actual vineyard. Learning outcomes were measured by decision-making accuracy, response time, and intrinsic motivation.The findings show that the IVR group did not significantly outperform the slideshow group in decision-making accuracy. However, the IVR group took more time to make decisions. This observation suggests an impact of immersion during the transfer to real-world situations. Additionally, the IVR group showed a higher level of intrinsic motivation than the slideshow group.These results suggest that although the immersion effect does not directly enhance learning outcomes for this cognitive objective, it does affect how knowledge is transferred to the real world. They also confirm that the positive impact of immersion is difficult to generalize and may depend on the nature of the knowledge. Still, the immersion effect significantly improves learner motivation. This consistent finding could be a key factor in long-term educational success. Further research exploring the nuanced effects of immersion on different learning strategies and educational objectives could offer new practical perspectives for the future of educational technologies
Multiscale modeling of mechanically recycled glass fiber reinforced polyamide 6 composites accounting for viscoelasticity, viscoplasticity, and anisotropic damage
Full text linkFiber-reinforced thermoplastic composites are valued for their strength-to-weight ratio, cost-effectiveness, and recyclability, highlighting the need for efficient recycling technologies amid environmental concerns. This study addresses these challenges
by examining the mechanical response of recycled glass fiber reinforced polyamide 6 composites and modeling their nonlinear, time-dependent behavior under complex loading conditions. Advanced nonlinear constitutive and multiscale models, initially
developed for conventional fiber composites, are adapted to capture the stochastic response of recycled materials. These models integrate viscoelasticity, viscoplasticity and damage in the polymer matrix and account for anisotropic damage in the strands,
addressing the heterogeneity introduced by the recycling process. A modified random sequential adsorption technique replicates the microstructures for nonlinear response modeling. Hypotheses based on microstructural investigations consider processing
effects that disrupt the initial chip woven structure and create matrix-rich areas. The model captures anisotropy and variability observed in experimental data, providing a reliable framework for predicting the performance of recycled thermoplastic com-
posites and improving the understanding of the relationship between microstructure and mechanical properties, with a focus on inelastic nonlinear behavior
Environment Spatial Restitution for Remote Physical AR Collaboration
Full text linkThe emergence of spatial immersive technologies allows new ways to collaborate remotely. However, they still need to be studied and enhanced in order to improve their effectiveness and usability for collaborators. Remote Physical Collaborative Extended Reality (RPC-XR) consists in solving augmented physical tasks with the help of remote collaborators. This paper presents our RPC-AR system and a user study evaluating this system during a network hardware assembly task. Our system offers verbal and non-verbal interpersonal communication functionalities. Users embody avatars and interact with their remote collaborators thanks to hand, head and eye tracking, and voice. Our system also captures an environment spatially, in real-time and renders it in a shared virtual space. We designed it to be lightweight and to avoid instrumenting collaborative environments and preliminary steps. It performs capture, transmission and remote rendering of real environments in less than 250ms. We ran a cascading user study to compare our system with a commercial 2D video collaborative application. We measured mutual awareness, task load, usability and task performance. We present an adapted Uncanny Valley questionnaire to compare the perception of remote environments between systems. We found that our application resulted in better empathy between collaborators, a higher cognitive load and a lower level of usability, remaining acceptable, to the remote user. We did not observe any significant difference in performance. These results are encouraging, as participants' observations provide insights to further improve the performance and usability of RPC-AR
A comparison of process damping modelling as local flank face interaction and as macroscopic modal feature in a time domain machining simulation
Full text linkDissipative components of tool-workpiece interaction are of major importance in cutting-related vibrations. At the macroscopic vibration scale, such dissipation is usually accounted for by additional generalized damping forces in the equation of motion of the system’s elastodynamics. A finer consideration at cutting edge scale would bring up a line-distributed force mostly of ploughing nature. These two scales are usually linked by analytical integration, involving simplifying kinematical assumptions. In the present work a comparative investigation is proposed, for a machining operation, considering both representations in a detailed time domain modeling framework. Tool’s cutting edges are represented in a discretized manner, i.e. split into numerous elementary cutters allowing for detailed tool-workpiece interaction force distribution. The matter removal process is modeled via dexel-based surface discretization coupled with finite element-based modal shapes, enabling a consistent machined surface generation representation. Finally, the equations of motion are formulated for modal degrees of freedom and solved by a time marching algorithm. Based on these analyses, the limitations of resulting process damping force terms representations are considered regarding vibrations and interaction force magnitudes
On the strain energy decomposition in phase field brittle fracture: established models and novel cleavage plane-based techniques
Full text linkThèse financée par ALM (Angers Loire Métropole) et ANR RockStorHy.This work offers a detailed examination of the phase field approach for modeling brittle fracture, emphasizing its theoretical foundations, mathematical descriptions, and computational strategies. Central to our discussion is an in-depth analysis of strain energy decomposition methods integral to phase field models. We introduce an innovative technique using a cleavage plane based degradation that has shown promising results under various loading scenarios. We meticulously evaluate each method’s inherent limitations and challenges to highlight their respective advantages and drawbacks across different loading scenarios. This review aims not only to catalog existing knowledge but also to pave the way for future research directions in the application of phase field approach to fracture analysis
Topology Optimization of Chip Inductor Using Density Method
No full textThis paper proposes a novel methodology of the topology optimization method considering eddy current effects. The method is applied on chip inductors modelled by the Finite Element Method (FEM). Aiming to meet a specified inductance value while minimizing eddy current losses, we employ a density-based approach to construct a continuous material distribution. The derivative of the objective function with respect to the material distribution is obtained using the adjoint variable method, then the material layout is iteratively updated via the L-BFGS-B algorithm. The proposed framework is validated on both single-turn and multi-turn inductor structures, achieving designs that satisfy the target performance within a limited number of iterations. A key innovation of this work lies in the integration of field-circuit coupling into the topology optimization framework, enabling the analysis of inductors under complex coil configurations involving both series and parallel connections. Additionally, we present an original derivation of the sensitivity formulation associated with the inductance value ensuring that the optimized inductance meets the design specification
Screen Printed Piezoelectric Transducers for Structural Health Monitoring of Curved Thick Composite Panels
Full text linkThis research focuses on the development and experimental validation of a novel printed
piezoelectric transducers network employed on a foreign object damage panel substructure
of an aircraft engine fan blade. The main goal of the work is to leverage the screen printing
technology to fabricate arrays of piezoelectric transducers and ultimately employ these trans-
ducers for operations, enabling the development of structural health monitoring methods for
the panel. The printed transducer is made up of a piezoelectric layer sandwiched between
two silver electrodes, each printed in a controlled manner. Upon printing and drying of the layers, the transducers undergo polarization. The electromechanical behaviour of the printed transducers, characterized using impedance measurements, exhibits high repeatability, thus indicating its potential for large scale industrial deployment. Following this, it is demon-strated that the transducers are capable of accurately sensing impact, which is one the mostcommon yet critical sources of damage to an engine fan blade. It is also shown that the printed transducers are able to detect acoustic emission events. The ability of the printed transducers to actuate and sense guided wave signals over a range of ultrasonic frequencies is also demonstrated. Furthermore, apart from the noticeable advantages of the non-intrusive nature, and negligible weight as compared to their traditional ceramic counterparts, the printed piezoelectric transducers can potentially be integrated into the manufacturing process in the future, and the presence of transducer arrays ensures the availability of other transducers in case of an individual failure during service. This innovative printing technol-ogy for PZT transducer networks thus holds significant promise in bridging the gap between research advancements and the industrial implementation of SHM technology
Coupled crystal plasticity-cohesive zone modeling of rock salt viscoplasticity
Full text linkRock salt, owing to its viscoplastic behavior and structural integrity under high pressure, is a promising candidate for safe and large-scale underground energy storage. This study presents a comprehensive numerical framework for modeling the viscoplastic deformation of rock salt, accounting for both intragranular and grain boundary (GB) deformation mechanisms. Intragranular deformation is modeled using a crystal plasticity approach governed by a power-law relation, capturing the activity of crystallographic slip systems. Concurrently, a cohesive zone model (CZM) is introduced to simulate grain boundary sliding (GBS) and opening via a rate-dependent traction–separation law. This modeling strategy enables a detailed analysis of the coupled interplay between crystal plasticity and intergranular decohesion phenomena
AI-driven advances in composite materials for hydrogen storage vessels: A review
No full textThis review provides a comprehensive examination of artificial intelligence methods applied to the design, optimization, and performance prediction of composite-based hydrogen storage vessels, with a focus on composite overwrapped pressure vessels. Targeted at researchers, engineers, and industrial stakeholders in materials science, mechanical engineering, and renewable energy sectors, the paper aims to bridge traditional mechanical modeling with evolving AI tools, while emphasizing alignment with standardization and certification requirements to enhance safety, efficiency, and lifecycle integration in hydrogen infrastructure. The review begins by introducing HSV types, their material compositions, and key design challenges, including high-pressure
durability, weight reduction, hydrogen embrittlement, leakage prevention, and environmental sustainability. It then analyzes conventional approaches, such as finite element analysis, multiscale modeling, and experimental testing, which effectively address aspects like failure modes, fracture strength, liner damage, dome thickness, winding angle effects, crash behavior, crack propagation, charging/discharging dynamics, burst pressure, durability, reliability, and fatigue life. On the other hand, it has been shown that to optimize and predict the characteristics of hydrogen storage vessels, it is necessary to combine the conventional methods with artificial intelligence methods, as conventional methods often fall short in multi-objective optimization and rapid predictive analytics due to computational intensity and limitations in handling uncertainty or complex datasets. To overcome these gaps, the paper evaluates hybrid frameworks that integrate traditional techniques with AI, including machine learning, deep learning, artificial neural networks, evolutionary algorithms, and fuzzy logic. Recent studies demonstrate AI’s efficacy in failure prediction, design optimization to mitigate structural risks, structural health monitoring, material property evaluation, burst pressure forecasting, crack detection, composite lay-up arrangement, weight minimization, material distribution enhancement, metal foam ratio optimization, and optimal material selection. By synthesizing these advancements, this work underscores AI’s potential to accelerate development, reduce costs, and improve HSV performance, while advocating for physics-informed models, robust datasets, and regulatory alignment to facilitate industrial adoption
Methods for Determining the Magnetic State of Permanent Magnets on Rotors, in a Perspective of End of Life of Electric Machines
No full textassocier à la bibliothèque du Network : EcoSDFaced with the rising number of electric vehicles, the recycling of permanent magnet (PM) rotors of electrical machines is a pivotal concern since PM, for this, application are generally made with Critical Raw Materials, i.e. rare earth materials. Therefore, the development of effective End of Life strategies for PM is essential to mitigate the environmental impact associated with their production and meet the rising demand sustainably.
This paper presents a method to reconstruct the magnetization state of the PM on site within the rotor based on external field measurements. This information will be really useful to evaluate the PM state of health in order to evaluate the possibility of reuse of the rotor or to recycle the PM. The process of reconstruction is based on an inverse method and it has been fully simulated using A Finite Element (FE) model for the rotor. It is shown on different rotor topologies (surface PM mounted rotor, PM buried rotor…) that it is possible to determine the magnetization state of the PM