KEYWORDS: Sensors, Control systems, Industry, Signal detection, Detection and tracking algorithms, Algorithm development, Process control, Sensor technology, Mathematical optimization
With the development of intelligent technology, the textile industry has introduced advanced sensors, control systems, and data analysis technologies to achieve more precise control operations, improve production efficiency, reduce manual labor, lower production costs, and enhance product quality and stability. Yarn tension control directly affects the production efficiency and product quality in the textile industry. Based on recent research progress in tension control, this article reviews the advancements in yarn tension sensors, tension detection, and control technologies. It summarizes the current development trends in yarn tension control technology and points out that the integrated research on the application of new sensor technologies, artificial intelligence technology, intelligent control, and novel control algorithms in tension control is crucial for achieving stable control and driving the continuous progress of tension control technology.
In modern industrial development and the era of intelligent production, fabric defects have become a crucial aspect of the textile industry. The accurate identification of defects on fabrics through computer vision technology holds significant research value. This article provides an overview of machine vision detection techniques for fabric defects. It classifies and analyzes existing defect detection techniques, summarizes achievements and innovations in recent years, and examines the research progress in defect detection. The article discusses various models and theories in detail, compares commonly used defect detection techniques, and identifies their main shortcomings. It analyzes the research status in the field of defect detection both domestically and internationally in recent years, provides a comprehensive discussion on relevant models and theories, compares them with current common defect detection techniques, highlights the imperfections in mainstream fabric defect detection, and offers predictions for future development trends.
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