Study on the Distribution of Frictional Forces on Z-yarn Continuous Implanted Preforms and Their Applications

To improve the quality and efficiency of Z-directional 3D preform forming, the Z-yarn frictional force distribution model of the preform and its wear mechanism were investigated. In this study, a tensile force measuring device was designed to measure the force required to replace the guide sleeve, which is equivalent to the Z-yarn frictional forces. The frictional force is proportional to the number of preform layers and is applied to the preform decreased from the corner, edge, sub-edge, and middle in order. A back propagation neural network model was established to predict the friction at different positions of the preform with different layers, and the error was within 1.9%. The wear of Z-yarn was studied at different frictional positions and after different times of successive implantation into the preform. The results showed that with an increase in the number of Z-yarn implantations and frictional forces, the amount of carbon fiber bundle hairiness gradually increased, and the tensile fracture strength damage of the fiber was increasingly affected by the frictional forces. In the corner position of the preform, when the number of implantations was 25, the fiber fracture strength decreased non-linearly and substantially; in order to avoid fiber fracturing in the implantation process, the Z-yarn needs to be replaced in time after 20-25 cycles of continuous implantation. This study solves the problem of difficulty in measuring the force required for individual replacements owing to the excessive number of guide sleeves, puts forward the relationship between fiber wear, preform position, and implantation times, solves the phenomenon of fracture in the preform during Z-direction fiber implantation, and realizes the continuous implantation of fibers.

Automated summary

This study investigated the Z-yarn frictional force distribution model and wear mechanism of Z-directional 3D preforms. A tensile force measuring device was designed to measure the frictional forces, and a neural network model was established to predict the friction at different positions of the preform. The study also examined the wear of Z-yarn at different frictional positions and after different times of implantation into the preform. The study's findings have important implications for improving the quality and efficiency of Z-directional 3D preform forming.