Explore IoT process monitoring in textile manufacturing through a published research reference related to Textile Quality Control. This B.Tech resource summarizes the available source and suggests questions to validate before choosing an implementation.
IoT process monitoring in textile manufacturing: source and project details
| Research overview |
This research describes a Hybrid IoT‑AI Framework (HIAF) that updates traditional textile factories. It tackles three big problems: a lot of manual work, uneven product quality, and wasted resources. The framework connects several kinds of sensors—RFID, optical, humidity, and vibration—to watch key steps in making fabrics. It covers spinning, weaving, dyeing, and finishing for cotton, polyester, and silk. Data are first processed on edge devices, so the system can react instantly. The results are then sent to the cloud where longer‑term analytics and predictions are run. An AI‑based anomaly detector looks for defects in fabric texture, dye uniformity, and fiber strength.
When a problem is found, automated control loops automatically tweak machine settings to keep quality steady. A digital twin of the production line is also built. It lets engineers run simulations and plan maintenance before a real failure occurs. The framework was tested in 50 factories across five major Indian textile hubs for six months. During the trial it cut a key performance metric by 32 % (the exact metric isn’t specified in the abstract). For B.Tech students, the material gives step‑by‑step advice on: – installing local sensor networks, – creating edge‑to‑cloud data pipelines, and – building simple machine‑learning models to detect textile defects.
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| Reference Paper |
IoT Driven Real-Time Process Monitoring and Intelligent Quality Control Systems in Textile Manufacturing |
| Publication year (source record) |
2026 |
| Source document classification |
Research reference (type not independently verified) |
| Discovered via |
Semantic Scholar metadata |
| Authors (source metadata) |
Kathirvel M, Chandrasekaran M |
| Publication type (source metadata) |
journal-article |
| Journal / conference (source metadata) |
EPJ Web of Conferences |
| Publisher (source metadata) |
EDP Sciences |
| DOI (from source metadata) |
10.1051/epjconf/202635404004 |
| Metadata consistency check |
DOI registration metadata compared with indexed record; original PDF text not independently verified. |
| Evidence reviewed by generator |
Indexed abstract/description only; original full text, author names in PDF and methods require editorial verification. |
| Domain |
Textile Engineering |
| Sub-Domain |
Textile Quality Control |
| Research source |
View research PDF |
| Get Help |
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Project or research direction
This is a source-backed research starting point, not a tested, ready-made student project. Any implementation requires independent feasibility, safety and results verification.
Possible tools or components to evaluate
ESP32/Raspberry Pi, DHT22 (Humidity), SW-420 (Vibration), RC522 RFID, USB Camera, Python, OpenCV, MQTT Protocol, Node-RED, ThingsBoard IoT Cloud
Methods or operating principles to examine
Multi-sensor data acquisition for environmental and mechanical parameters, edge-level preprocessing for real-time anomaly detection, optical fabric defect identification, cloud-based dashboard for predictive analytics, and simulated automated feedback loops for machine parameter adjustment.
Outputs to plan and verify
A functional hardware-software prototype demonstrating real-time sensor data streaming, an image processing pipeline capable of detecting simulated fabric texture anomalies, and a cloud dashboard displaying machine health and predictive maintenance alerts.
Further study ideas to evaluate
LoRaWAN integration for long-range communication in large-scale mill environments; mobile application alerts for floor managers; and reinforcement learning algorithms for optimized control loop adjustments.
Check the complete source before describing any of these as new or absent from the original work.
Suggested verification steps
- Read the cited paper and check which methods, parameters and results it actually reports.
- Define a measurable project objective and list equipment or software you can access.
- Validate the proposed approach independently before claiming results, working code or experimental success.
Implementation ideas above are planning suggestions, not claims that the cited researchers built or tested those exact extensions.
Explore related B.Tech project topics or browse the engineering resource library for different methods and reference papers.
