Predicting ramie yarn tenacity and prioritizing fiber property control during degumming: A numerical modeling approach

Degumming is an essential pretreatment for ramie fiber spinning; it removes the gum from ramie fibers and therefore alters their linear density, breaking strength, and length, which further determines the tenacity of the resultant yarns. To quantitatively predict ramie yarn tenacity, identify the priority of fiber property control in ramie degumming, and evaluate degumming performance, this work proposes a numerical model derived from mechanical analysis, incorporating the random arrangement of fibers, fiber properties (linear density, breaking strength, and length), and yarn parameters (linear density and twist factor). The model was validated against experimental data of ramie yarns spun from degummed fibers. These yarns covered three linear densities and a range of twist factors, with the model yielding MAPE below 5% and RMSE no more than 0.80 cN/tex. Sensitivity analysis based on the validated model revealed that fiber linear density exerted the most prominent influence on ramie yarn tenacity, followed by fiber breaking strength, while fiber length exerted a relatively minor influence. Further validation using fibers subjected to different degumming degrees confirmed both the robustness of the model across a broader range of fiber quality conditions and the applicability of this hierarchy to practical degumming scenarios. The established model offers a practical tool for predicting ramie yarn tenacity, provides a quantitative basis for prioritizing fiber property control during ramie degumming, and enables rapid and quantitative evaluation of degumming performance.