Defects such as strong twist, weak twist, oil stains, lint, and weft slippage can occur during the production of tire cord fabric, seriously affecting product quality and corporate reputation. Currently, the production process mainly relies on manual inspection, but due to the influence of environment, fatigue, and emotions on human operators, the quality of inspection cannot be guaranteed.
Online inspection of tire cord fabric looms uses machine vision technology to replace manual labor for comprehensive inspection of the fabric surface during the production process. Deep learning AI technology is employed to classify detected defects, triggering alarms or halting the machine based on the type of defect.

The system employs a unique imaging method that can acquire clear images of the fabric surface, thus eliminating the impact of on-site vibration on imaging.

Function:

1. Defect detection: stains, abnormalities, air joints, hand-sewn ends; weft slippage at the fabric edge; loose warp threads, stains, lint on the fabric surface; weft yarn stacking; warp yarn adhesion, broken weft, thick weft, insects and foreign objects;
2. Detected defects are permanently saved as images and can be viewed later;
3. It has functions such as defect location, data traceability, alarm prompts, and automatic shutdown;