A critical review of online sliver monitoring technologies in the draw frame of spinning preparation systems and future advances

The quality of sliver at the draw frame stage is a decisive factor in achieving yarn uniformity, particularly in air-jet (Vortex, MVS), Open-end rotor, and friction spinning, where drawn slivers are passed directly into the spinning unit without a roving frame. Current auto-levelling systems enhance sliver evenness and productivity but can only be used for mass control, not to measure more detailed fiber-level properties. Therefore, the current auto-levelling technologies cannot be completely extended to high-level decision-making, fault diagnosis in real-time, and intelligent optimization of draw frame passages in accordance with the real sliver quality. Traditional methods of assessing slivers rely on offline testing with equipment such as the USTER Tester, which is highly precise but incapable of identifying defects during manufacturing. In this review, the author evaluates current technologies for monitoring fibers, slivers, and yarns, including optical, capacitive, and vision-based systems, as well as IoT-enabled platforms in modern spinning mills. An overall evaluation of the existing limitations shows a significant gap in real-time monitoring of sliver quality. To satisfy this requirement, a theoretical framework of an online sliver testing system is suggested to be incorporated at the draw frame delivery. The system also has integrated high-quality sensors, real-time sensor processing, machine sensor integration, and smart feedback control. The review illustrates the possibilities that such a system has to improve the draw frame operation, minimize scraps, enhance the uniformity of yarns, and facilitate Industry 4.0 changeover in spinning processes. This review not only summarizes existing sliver monitoring technologies but also defines the technological gap between current draw frame control and the requirements of real-time intelligent monitoring in spinning, proposing a feasible framework for future industrial implementation. The suggested solution not only improves the quality evaluation in real-time and process efficiency but also outlines the basis of AI-based manufacturing systems in the textile industry in the future.