IoT Based Real-Time Moisture Management in Fabric Drying Processes

Ensuring product quality and production efficiency in textile manufacturing operations is greatly dependent on appropriate moisture control. There have been inefficiencies and less-than-ideal results from using traditional techniques to monitor and manage textile moisture levels since they do not have real-time capabilities. It provides a new method for controlling and monitoring fabric drying processes in real-time by combining the K-Nearest Neighbors (KNN) algorithm with Internet of Things (IoT) technology. Using the IoT infrastructure, sensors implanted in the fabric can continuously and accurately monitor the amount of moisture while the fabric dries. The KNN algorithm makes use of similarities across moisture profiles to forecast the best drying settings after processing and analyzing the data. Our method improves textile moisture management via experiments and validation. With the help of the IoT, drying settings may be dynamically adjusted in reaction to changes in moisture levels in real time, which improves process control and efficiency. Reducing drying times and energy usage without sacrificing product quality is made possible by the proactive decision-making made possible by the predictive capabilities of the KNN algorithm. To optimize fabric drying processes it proposes a system that combines IoT technology and machine learning algorithms in a synergistic way. Sustainable, productive, and high-quality textile production processes may be achieved in real-time with the help of the proposed method.