Lunjun Li’s research while affiliated with Zhejiang Sci-Tech University and other places

The diversity of weaving equipment has led to inconsistencies in communication protocols, impeding data collection and interoperability between devices, and ultimately reducing production efficiency. Additionally, fabric defects significantly impact product quality, while current visual inspection technologies are primarily reactive and traditional quality prediction methods often exhibit considerable errors. This study leverages the standardization and interoperability features of open platform communications unified architecture technology to facilitate data acquisition within the weaving department, establishing a reliable Internet of Things framework that supports subsequent fabric quality prediction, and optimizing the back propagation neural network through the K-means clustering algorithm and particle swarm optimization to predict the type and number of fabric defects. A comparative analysis with traditional BP and PSO-BP prediction models was conducted, ultimately verifying the feasibility of using OPC UA to transmit weaving data for fabric quality prediction. The research results demonstrate that using OPC UA technology enables the unified transmission of weaving equipment data, addressing the issue of heterogeneity in weaving department equipment. The K-means-PSO-BP model can effectively predict defects such as double weft, hundred feet, and broken warp with minimal error, achieving a root mean square error of less than 0.15.