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Multiscale mechanical analysis for biomimetic implant design based on Triply Periodic Minimal Surfaces (TPMS) lattices: Application to partial replacement of femoral bone
Full text linkMedical implants are a common treatment for orthopedic injuries. Their apparent stiffness can be reduced by using architected internal lattices to match the gradient stiffness of the bone, thereby avoiding postoperative biomechanical problems such as stress shielding. The use of TPMS-based lattice structures with smooth junctions offers the potential to tailor the apparent modulus of an implant while minimizing stress concentration throughout the microstructure. In this study, four TPMS-based unit cells are investigated, namely: Schoen’s Gyroid-like (sheet and skeletal), Schwartz’s Primitive, and Schoen’s IWP topologies. The objective of the investigation is to numerically replace a small region of a femoral bone, hereafter referred to as the area of interest (AoI). Multiscale approach is proposed for the 3D model of the femur. The latter consists of the global model (femur bone) and the local model (TPMS unit cell). The unit cells are selected to satisfy the elastic and mechanical loading requirements and are compared according to the von Mises stress distribution after applying periodic boundary conditions. A statistical analysis is performed and a function factor is proposed to facilitate the comparison. The developed methodology allows the design of customized and patient-specific implants when a large medical database is used due to the varying size and shape of patients’ bones
Thermal stability and degradation of a low refractive index photo-crosslinkable adhesive
No full textIn some laser assemblies, optical components are glued on their metal mount using a photocrosslinkable adhesive. All these components are submitted to medium to high temperatures for several hours upon laser's operation time. The subsequent thermal stresses endured by the adhesive could lead to its degradation, alter thus the functioning of the assembly and impact the alignment of the laser, key issues in laser applications. This work focuses on the investigation and the modeling of the lifespan, the degradation in the face of thermal stresses, particularly those generated by a laser, and of the ageing of a photocrosslinkable adhesive, PC373HA. The thermal characterization is performed using ThermoGravimetric Analysis and allows for the development, the validation and the comparison of three models based on Arrhenius' law to estimate the lifetime of adhesives as a function of temperature. A temperature of 150 °C is identified as a threshold to ensure limited degradation over the 15 h requested for laser operation endurances. The Ozawa-Flynn-Wall model and the Kissinger-Akahira-Sunose model correctly reproduce PC373HA's thermal behavior submitted to different thermal scenarii (temperatures, heating rates …). Both show that the higher the temperature, the faster the degradation process, approximately 10 min at 220 °C and 100 min at 180 °C, much less than the required 15 h. Besides, in the event of an incident with the laser, if the temperature exceeds 200 °C for more than 10 min or 180 °C for more than 100 min, corrective action should be taken, and the adhesive should be replaced. These models therefore provide fundamental information for laser applications and will allow the implementation of preventive solutions during use but also in case of incident. © 2025 Elsevier Lt
MemorIA, an Architecture for Creating Interactive AI Historical Agents in Educational Contexts
Full text linkThis article presents the architecture of MemorIA, an integrative system that combines existing AI technologies into a coherent educational framework for creating interactive historical agents, with the aim of fostering students' learning interest. MemorIA generates animated digital portraits of historical figures, synchronizing facial expressions with synthesized speech to enable natural conversations with students. The system leverages NVIDIA Audio2Face for real‐time facial animation with first‐order motion model for portrait manipulation, achieving fluid interaction through low‐latency audio‐visual streaming. To assess our architecture in a field situation, we conducted a pilot study in middle school history classes, where students and teachers engaged in direct conversation with a virtual Julius Caesar during Roman history lessons. Students asked questions about ancient Rome, receiving contextually appropriate responses. While qualitative feedback suggests a positive trend toward increased student participation, some weaknesses and ethical considerations emerged. Based on this assessment, we discuss implementation challenges, suggest architectural improvements, and explore potential applications across various disciplines
In Situ Monitoring of Retained Austenite Decomposition During Tempering of High-Strength Tool Steels
Full text linkThis study investigates the decomposition of retained austenite (RA) in tool steels for plastic molding in correlation with the alloy chemical composition and the tempering parameters. Two grades differing in their silicon content with initial mixed bainitic/martensitic microstructures were investigated using in situ synchrotron high-energy X-ray diffraction (HEXRD) during tempering in the 550 °C to 600 °C temperature range for one-hour holding time. Results indicated carbide formation during heating or isothermal holding; however, retained austenite remained untransformed up to the end of the tempering holding time in all investigated conditions for both grades. In situ HEXRD provides direct evidence of the transformation of retained austenite into fresh martensite on cooling from the tempering stage. This behavior is correlated to the evolution of carbon enrichment of retained austenite and the effect of silicon is discussed
Effect of different cryogenic lubrication methods on machinability of Ti6Al4V
Full text linkAbstract. In machining industry, there is a growing interest in cryogenic cooling techniques, because of their environmental benefits, including reduced toxicity, safer operation, and lower environmental impact compared to conventional cutting fluids. The titanium alloy Ti6Al4V, which is commonly used in aerospace, automotive and biomedical industries, presents low machinability and often requires abundant use of cutting fluids to inhibit tool wear. This study investigates the machinability of Ti6Al4V, comparing conventional lubrication (water-oil emulsion) with two cryogenic fluids: liquid nitrogen (LN2) and liquid carbon dioxide (LCO2). Longitudinal turning tests were conducted and tool life, wear mechanisms, and cutting forces were evaluated for each lubrication condition. The tool life provided by emulsion, LN2 and Vc were 8.2 min, 17.7 min and 9.9 min, respectively. Adhesion was identified as the predominant wear mechanism across all conditions. Overall, the results suggest that the cryogenic coolants can effectively increase tool life and reduce cutting forces in comparison with conventional lubrication, however, further optimizations of the delivery system of the cryogenic coolants are still necessary
Design of Multiphase Compositionally Complex Alloys for Enhanced Hardness at Elevated Temperatures and Machinability: Comparative Study with Inconel 718
Full text linkInconel 718 alloy is used for high‐temperature industrial applications in its optimized multiphase metallurgical state. Nevertheless, the machining of Inconel 718 alloy becomes problematic and challenging. One alternative consists of developing a new material design strategy based on the metallurgy of high‐entropy alloys (HEAs). These alloys have become a hotspot in the field of innovative high‐temperature metallurgy toward the improvement of the alloy's manufacturability and thermomechanical properties. This study aims at designing, elaborating, and characterizing a new class of alloys with increased entropy, referred to as: “Inco‐like.” The mechanical responses of the alloys, in terms of hardness, have been analyzed using an indentation test at a wide range of temperatures. The dry machinability of the developed alloys has been performed and compared with that characterizing the Inconel 718 in terms of several machining features. Finally, the phases of the studied alloys have been analyzed using metallurgical investigations. The experimental findings and comparisons underscore the advantages of the high‐entropy strategy in terms of tool wear reduction and cutting tool durability. The results demonstrate that the Inco‐like HEA retains a significantly higher hardness of 291 Hv at 800 °C, compared to 160 Hv for Inconel 718 at the same temperature
Elasticipy: A Python package for linear elasticity and tensor analysis
Full text linkElasticipy is a Python library designed to streamline computation and manipulation of elasticity tensors for materials and crystalline materials, taking their specific symmetries into account. It provides tools to manipulate, visualise, and analyse tensors –such as stress, strain and stiffness tensors– simplifying workflows for materials scientists and engineers
eCAD-Net: Editable Parametric CAD Models Reconstruction from Dumb B-Rep Models Using Deep Neural Networks
No full textThis paper introduces a novel framework capable of reconstructing editable parametric CAD models from
dumb B-Rep models. First, each B-Rep model is represented with a network-friendly formalism based on UVgraph,
which is then used as input of eCAD-Net, the new deep neural network-based algorithm that predicts
feature-based CAD modeling sequences from the graph. Then, the sequences are scaled and fine-tuned using
a feature matching algorithm that retrieves the exact parameter values from the input dumb CAD model. The
output sequences are then converted in a series of CAD modeling operations to create an editable parametric
CAD model in any CAD modeler. A cleaned dataset is used to learn and validate the proposed approach, and
is provided with the article. The experimental results show that our approach outperforms existing methods
on such reconstruction tasks, and it outputs editable parametric CAD models compatible with existing CAD
modelers and ready for use in downstream engineering application
Comprehensive review of multi-scale Lithium-ion batteries modeling: From electro-chemical dynamics up to heat transfer in battery thermal management system
Full text linkThe growing development of lithium-ion battery technology goes along with the new energy storage era across various sectors, e.g., mobility (electric vehicles), power generation and dispatching. The need for sophisticated modeling approaches has become a crucial tool to predict and optimize battery behavior given the demand of ever-higher performance, longevity, and safety. This review integrates the state-of-the-art in lithium-ion battery modeling, covering various scales, from particle-level simulations to pack-level thermal management systems, involving particle scale simplifications, microscale electrochemical models, and battery scale electrical models with thermal and heat generation prediction. Beyond that, authors highlight the growing trend in integrating highly accurate physics-based with thermal approaches such as the electrochemical-thermal coupled model to fully answer the multiscale challenges. Through capturing the electrochemical phenomena and thermal dynamics, and developing a comprehensive understanding of battery kinetics, safety risks such as thermal runaway can be thoroughly mitigated. Authors emphasize the trade-offs between computational efficiency and model complexity, explaining the limitations, strengths, and applications of diverse modeling approaches. This review illuminates the integration of battery management systems and cooling strategies
Multiscale Thermodynamics-Informed Neural Networks (MuTINN) for nonlinear structural computations of recycled thermoplastic composites
Full text linkFiber-reinforced thermoplastic composites are increasingly valued for their light-weight properties, mechanical performance, and recyclability, yet the recycling process introduces microstructural heterogeneities that degrade their mechanical behavior. To address the challenges from a modeling point of view, this study proposes a Multiscale Thermodynamics-Informed Neural Network (MuTINN) approach to predict the nonlinear, anisotropic response of recycled glass fiber-reinforced polyamide 6 composites, with the primary aim of enabling structural simulations in significantly reduced time compared to traditional FE² approaches. The MuTINN framework integrates thermodynamic principles with artificial neural networks (ANNs) to capture the evolution of internal state variables and Helmholtz free energy, eliminating the need for memory-based networks. Finite element simulations of representative
volume elements (RVEs) under diverse loading conditions are utilized to provide off-line data for the MuTINN. The latter accurately predicts stress, strain, and energy quantities, accounting for the anisotropic and heterogeneous nature of recycled materials. While trained using numerical simulations at 0◦ and 90◦ orientation specimens, the proposed framework succesfully predicts the response for specimens with 45◦ orientation with error in the maximum stress level up to 1.6%. The model is implemented into commercial finite element analysis (FEA) software via a Meta-UMAT framework, allowing efficient macroscale simulations. Validation against experimental data and finite element-based periodic homogenization confirms the framework’s accuracy for structural computations