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Genevieve Palardy

Publications and source records attributed to Genevieve Palardy.

4 recordsLinked to original sources

History Matters: Damage-Mediated Amplification of Brain Deformation and Injury Risk under Repeated Head Impacts

Computational head models are typically applied to isolated impacts, leaving repeated head loading largely unexplored. An Ogden-Roxburgh Mullins damage formulation was implemented in a high-fidelity finite element head model to represent loading-history-dependent softening during cyclic brain-tissue deformation. Repeated-loading histories derived from mixed martial arts head-impact data were applied and compared with damage-free hyperelastic (HE) and linear visco-hyperelastic (LVHE) model variants. Under five identical single-axis cycles, Mullins-type softening progressively increased strain and strain rate metrics relative to the HE model. Mullins-based injury probabilities progressively exceeded strain-based HE predictions and diverged from unchanged kinematics-based predictions, indicating that neglecting prior softening may underestimate injury risk. In a randomized twenty-cycle multiaxial sequence, cycles of similar kinematic intensity produced different deformation and injury-risk estimates depending on prior softening. HE and LVHE models predicted higher injury probabilities initially, whereas the Mullins-based model produced the largest later-cycle estimates and highest probability of at least one injury over the sequence. Regional amplification depended on loading direction and prior softening, with no direction-independent trend among brain substructures. Gyral elements exhibited higher cumulative maximum principal strain than sulcal elements, which showed greater amplification relative to initial responses. These findings demonstrate that short-term damage-mediated softening can substantially amplify tissue deformation and injury-risk estimates beyond damage-free head models under the same loading histories. Further experimental characterization of cyclic brain-tissue softening is needed to improve models of repeated head loading and traumatic brain injury.

q-bio.TO

Mechanical and electrical properties of MWCNT/PP films and structural health monitoring of GF/PP joints

While welding of thermoplastic composites (TPCs) is a promising rivetless method to reduce weight, higher confidence in joints' structural integrity and failure prediction must be achieved for widespread use in industry. In this work, we present an innovative study on damage detection for ultrasonically welded TPC joints with multiwalled carbon nanotubes (MWCNTs) and embedded buckypaper films. MWCNTs show promise for structural health monitoring (SHM) of composite joints, assembled by adhesive bonding or fusion bonding, through electrical resistance changes. This study focuses on investigating multifunctional films and their suitability for ultrasonic welding (USW) of TPCs, using two approaches: 1) MWCNT/filled polypropylene (PP) nanocomposites prepared via solvent dispersion, and 2) high conductivity MWCNT buckypaper embedded between PP films by hot pressing. Nanocomposite formulations containing 5 wt and 10 wt MWCNTs were synthesized using solvent dispersion method, followed by compression molding to manufacture films. The effect of MWCNT concentration on electrical and dynamic mechanical behavior of multifunctional films was examined with a Sourcemeter and Dynamic Mechanical Analyzer, and a comparison was made between 5 to 20 wt MWCNT-PP films based on previous research. Glass fiber/polypropylene (GF/PP) composite joints were ultrasonically welded in a single lap shear configuration using buckypaper and MWCNT/PP films. Furthermore, electrical resistance measurements were carried out for joints under bending loads. It was observed that 15 wt and 20 wt MWCNT/PP films had higher stability and sensitivity for resistance response than embedded buckypaper and films with low MWCNT contents, demonstrating their suitability for USW and potential for SHM.

physics.app-ph

Design and integration of end-effector for 3D printing of novel UV-curable shape memory polymers with a collaborative robotic system

This paper presents the initial development of a robotic additive manufacturing technology based on ultraviolet (UV)-curable thermoset polymers. This is designed to allow free-standing printing through partial UV curing and fiber reinforcement for structural applications. The proposed system integrates a collaborative robotic manipulator with a custom-built extruder end-effector designed specifically for printing with UV-curable polymers. The system was tested using a variety of resin compositions, some reinforced with milled glass fiber (GF) or fumed silica (FS) and small-scale, 2D and 3D specimens were printed. Dimensional stability was analyzed for all formulations, showing that resin containing up to 50 wt% GF or at least 2.8 wt% FS displayed the most accurate dimensions.

cs.RO

Next-generation perception system for automated defects detection in composite laminates via polarized computational imaging

Finishing operations on large-scale composite components like wind turbine blades, including trimming and sanding, often require multiple workers and part repositioning. In the composites manufacturing industry, automation of such processes is challenging, as manufactured part geometry may be inconsistent and task completion is based on human judgment and experience. Implementing a mobile, collaborative robotic system capable of performing finishing tasks in dynamic and uncertain environments would improve quality and lower manufacturing costs. To complete the given tasks, the collaborative robotic team must properly understand the environment and detect irregularities in the manufactured parts. In this paper, we describe the initial implementation and demonstration of a polarized computational imaging system to identify defects in composite laminates. As the polarimetric images are highly relevant to the surface micro-geometry, they can be used to detect surface defects that are not visible in conventional color images. The proposed vision system successfully identifies defect types and surface characteristics (e.g., pinholes, voids, scratches, resin flash) for different glass fiber and carbon fiber laminates.

cs.RO