INNOVATIVE METHODS FOR AUTOMATED WARP YARN QUALITY CONTROL AND THEIR IMPACT ON WEAVING EFFICIENCY - Muslimbek Abdujabborov (Web of Journals Publishing, 2025)
This study investigates the implementation of an integrated automated control system for warp yarn quality, combining computer vision and multi-sensor data fusion. Traditional manual inspection methods are prone to subjectivity and inefficiency, leading to undetected defects that cause yarn breaks and loom stoppages. The proposed system utilizes high-resolution line-scan cameras and tension sensors to continuously monitor yarn diameter, hairiness, and tension in real-time. A machine learning-based algorithm classifies defects and predicts potential breakage points. Experimental results demonstrate a 45% reduction in warp breaks and a 15% increase in overall equipment effectiveness (OEE) compared to conventional methods, highlighting the significant potential of automated systems for enhancing weaving productivity and product quality.
Keywords: algorithm; computer vision; multi-sensor data