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The aim is to implement a Hybrid IoT-AI Framework to enhance quality control and reduce waste in textile production.

March 10, 2026

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

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The aim is to implement a Hybrid IoT-AI Framework to enhance quality control and reduce waste in textile production.
Implemented a Hybrid IoT-AI Framework in 50 textile manufacturing plants over six months.
Integrated RFID, optical, humidity, and vibration sensors for real-time monitoring.
Utilized edge computing for data processing and cloud analytics for predictive insights.
Developed a detection module for texture, dye consistency, and fiber strength anomalies using AI.
Achieved a 32% reduction in product defects after implementation.
Realized a 28% increase in first-pass yield.
Reduced operational downtime by 25% due to real-time system adjustments.

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Implication

This research demonstrates an IoT-AI framework improving quality control and reducing defects in textile manufacturing, suggesting enhanced efficiency.

Cite This Study

M et al. (2026) studied this question.

synapsesocial.com/papers/69af952b70916d39fea4c7be https://doi.org/10.1051/epjconf/202635404004/pdf

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Implication

This research demonstrates an IoT-AI framework improving quality control and reducing defects in textile manufacturing, suggesting enhanced efficiency.

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

Key Points

Discussion

Implication

Cite This Study

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

Key Points

Discussion

Implication

Cite This Study

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