Co-authors​ :

 Yu WANG (FRANCE), Sascha KRUGL , Yanan JIAO , Libo YAN , Peng WANG  

The production process of textile preforms involves a friction behavior that is exceedingly non-linear. This behavior is governed by a number of factors at the yarn level, which are in turn driven by interactions at the fiber level. The prediction approach for fiber and yarn level is an essential component in the process of enhancing the mechanical properties of the composites that have been produced. To optimize the production process, it is also a technique that is cost-effective. The friction that occurs between yarns has been shown to have a major impact on the velocity of friction, the amount of force that is applied, and the starting tension, according to study that was recently conducted on the subject of improving mechanical properties. It is possible to see these effects using either experimental or theoretical means. However, a single approach is not appropriate to investigate the causes of friction since there are several factors that are interrelated with one another. It should be considered to utilize a combination of modeling and experimental methodologies, based on the findings of past research, to accurately estimate the friction mechanism in complex situations.
The present research introduces an innovative multi-scale modeling approach that accurately predicts the friction and wear characteristics of fiber. The modeling approach encompasses the utilization of both geometric and mechanical components. A micro–meso scale yarn model is created by utilizing the Temusinko beam element. The objective of the local geometry modification method is to enhance the robustness of contact behavior by utilizing yarn distance functions based on micro-CT reconstruction technology. Moreover, a stochastic damage mechanical model of the yarn was developed to replicate the friction and wear characteristics of the fiber, resulting in an improved depiction of actual situations. This modeling approach is used to determine the orthogonal friction behavior with various yarn constructions. Furthermore, the effectiveness of the simulation is validated by the experimental methodology. The model validation demonstrates that the suggested simulation technique properly predicts friction behavior. The friction and wear characteristics of yarn in various preform architectures are anticipated based on geometry and mechanics. Additionally, the development of the aforementioned properties is quantitatively quantified using different failure modes. This modeling method provides a helpful tool for understanding the mechanical response of yarns.