In Situ Surface Defect Detection in Polymer Tube Extrusion: AI-Based Real-Time Monitoring Approach

Area of Science:

• Manufacturing Engineering
• Industrial Technology
• Human Factors Engineering

Background:

• The global aging workforce presents challenges to smart manufacturing, particularly impacting visual inspection crucial for quality control.
• Declining visual perception due to aging can reduce accuracy and efficiency in manufacturing quality assurance processes.
• Addressing these age-related visual changes is essential for maintaining high productivity and product quality.

Purpose of the Study:

• To investigate the use of real-time monitoring technology combining cameras and artificial intelligence (AI) to mitigate the effects of aging on visual recognition in polymer tube production.
• To develop a solution that compensates for age-related declines in visual perception within a smart manufacturing context.
• To enhance defect detection and identification capabilities in polymer tube manufacturing.

Main Methods:

• Implementation of strategically positioned cameras integrated with sophisticated AI algorithms for real-time monitoring.
• Development of a system for immediate defect detection, identification, and response in polymer tube production lines.
• Utilizing advanced AI for high-speed and accurate analysis of visual data.

Main Results:

• The developed system achieved excellent defect detection performance, with approximately 99.24% accuracy.
• Simultaneous defects in polymer tubes were accurately detected in real time with a processing speed of approximately 20 ms.
• The technology demonstrated the capability to respond immediately to detected defects, minimizing downtime.

Conclusions:

• Real-time monitoring technology, enhanced by AI and cameras, effectively mitigates the negative impact of decreased visual perception in aging workers.
• The system is expected to significantly improve quality consistency and the overall efficiency of quality management in manufacturing.
• This adaptive and high-performance technology offers a viable solution for smart manufacturing systems facing workforce aging challenges.