IoT Driven Real-Time Process Monitoring and Intelligent Quality Control Systems in Textile Manufacturing

This research introduces the implementation of a Hybrid IoT-AI Framework (HIAF) to promote the integration of Industry 4.0 in textile production to deal with the critical issues of labour-intensive, inconsistent quality, and waste of resources. The system incorporates the integration of RFID, optical, humidity, and vibration sensors of adding of the sensors of the RFI, optical, humidity and vibration with the smart network of the monitoring in real time at all the important production processes of spinning, weaving, dyeing and finishing; for different types of fabric: cotton, polyester, silk. The sample population was comprised of 50 manufacturing plants in five big Indian textile centers in a period of six months. The design uses edge computing to perform real-time data processing and cloud analytics to make predictive data. A texture, dye consistency, and fiber strength anomaly detection module is based on AI and automated control loops modify machine parameters in real-time. It is an innovative production line of digital twin that is used in simulation and predictive maintenance. Comprehensive evaluation has shown the framework's significant impact resulting in a 32% reduction of product defects; a 28% increase in first-pass yield and a 25% reduction in operational downtime.