arXiv subjects

Yanneck Wielhorski

Publications and source records attributed to Yanneck Wielhorski.

6 recordsLinked to original sources

arXiv2026

Periodicity and image registration for yarn path extraction in large 3D textiles

Accurate identification of yarns in X-ray computed tomography volumes remains a critical and complex step in generating high-fidelity numerical models of woven composites. This work introduces a tracking framework that leverages the intrinsic periodicity of woven architectures to transform a complex, large-scale segmentation problem into the annotation of a single representative unit cell. The approach first exploits the periodic nature of the weave to extract a representative unit cell from the volumetric data and segment it to provide a reference description of the yarn geometry. Digital Volume Correlation (DVC) is then performed between an idealised periodic volume obtained by replication of the unit cell and the real composite volume, yielding a three-dimensional displacement field that captures geometric deviations from ideal periodicity. The unit-cell yarn segmentation is propagated to the full volume by exploiting the periodicity of the reference volume, and the annotations are transported to the actual volume using the DVC-derived displacement field. Results obtained on real composite datasets demonstrate the ability of the method to accurately recover warp and weft yarn architectures with minimal input, opening new perspectives for efficient and scalable textile composite characterisation.

eess.SP↗

arXiv2026

Yarn tracking of large-scale 3D textile reinforcements using topological material features

Automated segmentation of CT images has become increasingly important to enhance the reliability of simulations through the generation of high fidelity numerical models. This study addresses the challenging task of semi-automatically tracking textile reinforcements in fan blade dry preforms using X-ray CT images captured at coarse resolutions (i.e., above 140 $μ$m). Our approach offers a scalable, slice-based analysis conducted on planes orthogonal to the main yarn directions, applied to a large-scale real industrial component. This enables accurate identification and tracking of yarn paths while requiring minimal training. The method models three key yarn properties statistically: their typical cross-section shape, their continuity and movement in the 3D space, and their spatial relative arrangement with respect to neighboring yarns. These statistical properties are integrated into a tracking framework via a variational formulation that optimizes all yarn center positions in successive cross-section planes. The method tracks more than 3,000 warp yarns across 1,500 slices and achieves a tracking success rate above 90%. Overall, this work demonstrates a promising approach toward large-scale, automated textile reinforcement annotation, paving the way for more efficient material characterization in complex composite structures.

cs.CV↗

arXiv2021

Descriptive Modeling of Textiles using FE Simulations and Deep Learning

In this work we propose a novel and fully automated method for extracting the yarn geometrical features in woven composites so that a direct parametrization of the textile reinforcement is achieved (e.g., FE mesh). Thus, our aim is not only to perform yarn segmentation from tomographic images but rather to provide a complete descriptive modeling of the fabric. As such, this direct approach improves on previous methods that use voxel-wise masks as intermediate representations followed by re-meshing operations (yarn envelope estimation). The proposed approach employs two deep neural network architectures (U-Net and Mask RCNN). First, we train the U-Net to generate synthetic CT images from the corresponding FE simulations. This allows to generate large quantities of annotated data without requiring costly manual annotations. This data is then used to train the Mask R-CNN, which is focused on predicting contour points around each of the yarns in the image. Experimental results show that our method is accurate and robust for performing yarn instance segmentation on CT images, this is further validated by quantitative and qualitative analyses.

cs.CV↗

arXiv2012

Bubble Shape and Transport During LCM Processes: Experimental Modeling in a T-Junction Tube

Long fiber composite materials can be elaborated by Liquid Composite Molding (LCM), a family of processes where fibrous preforms are injected by a low viscosity resin. During this process, we have to pay particular attention to the void formation inside the preform because it could modify the material final characteristics. Indeed, a preform presents two different porosity scales: between yarns called macropores and inside yarns, namely micropores.

cond-mat.soft↗

arXiv2012

Theoretical and Experimental Modelling of Bubble Formation with Connected Capillaries in Liquid Composite Moulding Processes

The void prediction in LCM processes sparks off interest within the composite material industry because it is a significant issue to keep the expected mechanical properties. The liquid properties, the preform geometry and the flow conditions impact the quantity of void entrapped inside the final product. The complex geometry of the reinforcement due to the arrangement of the bundles and the fibres is a key point to understand and quantify this phenomenon. This paper deals with both simple model networks which can occur inside a fabric representing connected capillaries, so-called "Pore Doublet Model (PDM)". A first is considering two capillaries converging on a node (T-junction) and a second is representing two capillaries interconnected with a supplying principle. These configurations can affect locally the evolution of flow fronts. First, experiments of bubble formed in a T-junction device have been performed and studied. Then a theoretical approach was proposed to forecast microvoid and macrovoid formation, by taking into account a supplying principle and arranged Washburn equation in forced filling.

cond-mat.soft↗

arXiv2012

Void Prediction During Liquid Composite Molding Processes: Wetting and Capillary Phenomena

The aim of this work is to contribute in improving fibrous preforms impregnation for Liquid Composite Molding (LCM) processes. The void prediction in LCM sparks off interest within the Composite Material elaboration because it represents a significant issue to keep the expected mechanical properties of the final product. The liquid properties, the preform geometry and the flow conditions impact the void or bubble entrapped inside and outside the yarns. Nevertheless, due to the complex geometry of the reinforcement, experimental characterization of bubble formation remains delicate. Thus, our study deals with two simple model networks representing connected pores so called "Pore Doublet Model". A first is considering two capillaries converging on a node (T-junction) and a second is representing two capillaries interconnected with a supplying principle. In this paper, we emphasize on microfluidic and millifluidic approaches where wetting and capillary forces are significant during bubble formation mechanism.

cond-mat.soft↗