Volume 15, Issue 8 · IJARST · ISSN 2319-1783 / 2320-1126

Smart Warp Tension Tracking Through Wireless Sensor Networks

Smart Warp Tension Tracking Through Wireless Sensor Networks is an open-access, peer-reviewed research paper by Vignesh S, Dr. Rathinavel S, published in Volume 15, Issue 8 of the International Journal of Advanced Research in Science and Technology (IJARST), a UGC-approved journal (Print ISSN 2319-1783, Online ISSN 2320-1126).

Author

Vignesh S, Dr. Rathinavel S

Abstract

Preserving ideal warp tension is essential in contemporary textile production to ensure excellent material quality and prevent loom downtime. However, complex connection structures or infrequent hand testing are intrinsic to classic tension surveillance systems. Due to their absence of spatial adaptability and real-time responsiveness, these traditional approaches often result in undiscovered tension anomalies and thread breakages. This research presents a unique paradigm for investigating dynamic stress fluctuations using Smart Warp Tension Monitoring Using Wireless Sensor Networks (WSNs) in order to get around these obstacles. The suggested design seamlessly records at 100Hz and communicates real-time strain data by placing a decentralized network of small, low-power sensor nodes right over the 2.5-meter warp sheet. In order to facilitate the framework to minimize mechanical noise and detect pressure variations within 15milliseconds before serious malfunctions, this work investigates new techniques in wireless data aggregation and edge computing. By using a 2.4GHz frequency hopping technique, the system can send messages with 99.9% reliability even in high-EMI situations. We were able to obtain a 40% decrease in data processing demands and increase operating battery life to over 6,000 hours by implementing lightweight Kalman filtering algorithms on each node. This ensures long-term industrial deployment scalability. The suggested WSN-based system operates much better than traditional monitoring mechanisms, according to experimental studies. The digital framework provides a dependable and accessible approach that opens the door to fully-computerized, IoT-driven textile production by achieving an 85% increase in error detection response time and a 24% decrease in entire material faults.

Keywords: Smart Textile Manufacturing, Wireless Sensor Networks (WSN), Edge Computing, Industrial Internet of Things (IIoT), Real-Time Error Detection and Warp Tension Monitoring.

DOI; https://doi.org/10.62226/ijarst20262759

References

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DOI

10.62226/ijarst20262759

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How do you cite this paper?

Vignesh S, Dr. Rathinavel S — “Smart Warp Tension Tracking Through Wireless Sensor Networks.” International Journal of Advanced Research in Science and Technology (IJARST), Volume 15, Issue 8. DOI: 10.62226/ijarst20262759.

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