Advances in yarn tension control for warp-knitting progress

This study summarizes a three-dimensional logical framework of cause–detection–compensation for tension research by reviewing the issue of yarn tension fluctuation in warp knitting, which should help subsequent researchers to quickly grasp the development context of yarn tension research. The dynamic tension imbalance originates from systemic mismatches in yarn supply–demand systems, requiring balanced coordination between yarn delivery volume and tension regulation in industrial production. Traditional empirical formula-based prediction models demonstrate theoretical constraints in responding to instantaneous abrupt demand changes in knitting zones, whereas existing electronic yarn-feeding systems show inadequate responsiveness to stepwise demand variations. Intelligent compensation focuses on two key directions: (1) integrating machine learning to develop multivariate regression models correlating fabric parameters with yarn properties for optimized supply–demand prediction; and (2) designing adaptive tension compensation devices to address sudden demand fluctuations. Limited by warp beam inertia, future research should prioritize the development of high-precision demand prediction models, active compensation mechanisms, and enhanced detection systems to advance precision-oriented textile machinery development.