Fabric Inspection System For Yunus Textile Mills Limited

Yunus Textile Mills Limited – YTML, a leading textile manufacturer, faced ongoing challenges with its traditional fabric inspection process.

Defect Detection

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Faster Inspections

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About YTML

In the highly competitive textile industry, ensuring consistent fabric quality is crucial for maintaining customer satisfaction and meeting production demands. Yunus Textile Mills Limited – YTML, a leading textile manufacturer, faced ongoing challenges with its traditional fabric inspection process. Manual inspections were time-consuming, prone to human error, and struggled to keep up with the increasing production volume. This not only affected the quality of the final products but also created inefficiencies in their operations.

Recognizing the need for a more effective solution, YTML collaborated with The Disrupt Labs to implement an AI-powered Fabric Inspection System. This innovative solution combined traditional inspection methods with advanced AI technology to deliver a faster, more accurate, and scalable approach to defect detection.

The Challenge

The manual fabric inspection process at YTML faced several problems.

01.

Inconsistent Quality Checks

The manual inspection process often missed small but important defects in the fabric. This inconsistency affected the overall quality of the products.

02.

Slow and Inefficient Process

Manual checks took a lot of time, slowing down the entire production line. This delay made it harder to meet deadlines and increased operational costs.

03.

Difficulty Scaling Operations

The inspection process could not keep up as demand for YTML’s products grew. The manual system was not scalable, which made it challenging to increase production without compromising on quality.

“The AI-based PPE Monitoring System from The Disrupt Labs has transformed our approach to workplace safety. Automating PPE monitoring has minimized human error, brought compliance levels to over 95%, and saved us countless hours previously spent on manual tracking. The system’s intuitive dashboard and automated reporting allow our management team to make informed, data-driven decisions, significantly boosting both safety and operational efficiency. It has truly set a new benchmark for safety at our facility.”

M/S Axens

Representative

The Solution

The Disrupt Labs developed a customized AI-powered Fabric Inspection System for YTML, combining traditional inspection techniques with advanced AI technology. This innovative solution significantly improved the speed and accuracy of defect detection, streamlining the entire inspection process.

Classical Defect Detection

Initially, the system used traditional algorithms to detect fabric defects. While this method had some success, it was not enough to handle the variety of fabrics and defects that YTML dealt with.

AI-Driven Defect Detection

By integrating machine learning, the system became highly efficient at identifying even the smallest defects quickly and accurately. This made it possible to maintain consistent quality across different fabric types and patterns.

User-Friendly Interface

The system included a simple dashboard where managers could monitor real-time data, see defect maps, and generate reports easily. This helped streamline the inspection process and made it easier for staff to use the system effectively.

Hardware Integration

High-resolution cameras and proper lighting systems were installed on the fabric rolling machines to ensure every defect was visible. This hardware upgrade was crucial in enhancing the system’s accuracy.

Real-Time Reporting

The system provided real-time updates and reports on fabric quality. This allowed YTML to take immediate action whenever a defect was detected, reducing wastage and improving overall production efficiency.

The Outcome

The Fabric Inspection System delivered significant improvements in quality control and operational efficiency

Better Defect Detection

From a selected range of defects, the system detected over 95% of defects, including those that were previously missed during manual inspections. This helped ensure that only high-quality fabrics were produced.

Faster Inspections

Comparing the speed of the existing manual inspection machine with our AI-integrated inspection machine reduced the inspection time by 50%, so production could move faster without sacrificing quality. This increase in speed helped YTML meet its production deadlines more efficiently.

Actionable Insights

The real-time reporting feature provided valuable insights that helped YTML quickly address any issues. This proactive approach not only improved product quality but also reduced the amount of defective fabric, saving time and resources.

Scalability

The system was designed to handle various types of fabrics and could easily scale with YTML’s growing production demands.

Conclusion

With the help of The Disrupt Labs, YTML successfully transformed its fabric inspection process. The AI-powered system made inspections faster, more accurate, and scalable, allowing YTML to maintain high-quality standards while meeting increasing production demands. This innovation positioned YTML as a leader in quality and efficiency within the textile industry.

The collaboration between YTML and The Disrupt Labs highlights the impact of AI on improving fabric inspection. By blending traditional inspection techniques with advanced AI, YTML achieved faster, more accurate defect detection, streamlined processes, and the ability to scale production easily. This AI-powered system has helped YTML maintain high-quality standards while boosting operational efficiency. This case study shows how AI can simplify complex processes and drive better results in the textile industry.