loader
banner

Product Quality Prediction in Textiles Manufacturing

Problem

In textiles manufacturing, detecting defects on Work-in-Progress (WIP) and finished goods is critical for ensuring product quality. Early detection of defects enables operators to take corrective actions promptly, reducing wastage and improving shop floor margins. The challenges faced in textiles manufacturing include:

  • Defect Detection in Fabric Cutting: Detecting defects in fabric during the cutting process is crucial to avoid further wastage and ensure only defect-free pieces are used for production.
  • Improving Stitching Productivity: Enhancing the productivity of stitching operations requires identifying and eliminating defects early in the production process.

Approach

To address the challenges and improve product quality prediction in textiles manufacturing, the following approach was adopted:

  • Real-Time Defect Detection using PlantBrain: A real-time defect detection and actuator response system was built for the fabric cutting machine using the PlantBrain system. During the fabric cutting process, real-time images of the moving fabric are acquired. 
  • Image Processing and Defect Recognition: The acquired real-time images undergo image processing and are analyzed using advanced computer vision algorithms. These algorithms recognize defects on the fabric by identifying predefined patterns or characteristics associated with defects. 
  • Integration with PLC: The defect detection solution is seamlessly integrated with the Programmable Logic Controller (PLC) of the cutting machine. Whenever the defect detection system identifies a defect, it sends a trigger signal to the PLC. 
  • Automated Actuator Response: Upon receiving the trigger signal from the defect detection system, the PLC of the cutting machine instantly stops the cutting process. This automated actuator response prevents any further damage to the fabric and avoids the production of defective pieces. 
  • Operator Intervention: The cutting machine operator is immediately alerted about the detected defect. The operator can visually inspect the fabric to identify the exact location of the defect. 
  • Selective Cutting for Waste Reduction: Armed with information about the location of the defect, the operator selectively cuts out only the defective portion of the fabric. The rest of the fabric remains unaffected, significantly reducing wastage and optimizing material usage.

Benefit

The implementation of real-time defect detection and automated actuator response in textiles manufacturing provides several benefits:

  • Improved Product Quality: Early defect detection allows operators to take corrective action promptly, ensuring that only defect-free fabric is used in production, leading to improved product quality. 
  • Reduced Wastage: Detecting defects during fabric cutting and allowing selective cutting of the defective portion significantly reduces wastage, saving materials and costs. 
  • Increased Productivity: The early detection of defects in the cutting process improves overall productivity by preventing the use of defective fabric in stitching operations. 
  • Cost Savings: By reducing wastage and enhancing productivity, the textiles manufacturing company saves costs and improves shop floor margins. 
  • Data-Driven Decision Making: The defect detection system provides data on defect occurrences, enabling data-driven decision-making for process optimization and quality improvement. 
  • Enhanced Customer Satisfaction: Ensuring higher product quality through defect detection leads to increased customer satisfaction and loyalty.

By implementing this real-time defect detection and automated actuator response system, the textiles manufacturing company can ensure improved product quality, reduced wastage, and increased productivity, leading to cost savings and enhanced customer satisfaction.