ADVANCED TECHNIQUES FOR DETECTING DEFECTS IN TEXTILE FABRICS
Keywords:
Textile defects, machine learning, computer vision, quality control, statistical process control, sustainable manufacturing, knitwear production, automated inspection.Abstract
In the textile industry, detecting fabric defects is crucial for maintaining product quality and ensuring efficient production. With the integration of advanced technologies, including machine learning, computer vision, and statistical process control (SPC), textile manufacturers can significantly enhance their defect detection capabilities. This paper explores various modern techniques used to detect defects in textile fabrics, focusing on methods that utilize machine learning algorithms, image processing, and integrated quality control systems. These methods not only improve defect detection accuracy but also reduce manual inspection time and minimize human error. Furthermore, the paper examines the role of sustainable quality control practices in ensuring long-term efficiency and reducing waste in the production process. The study synthesizes key research findings to provide a comprehensive overview of state-of-the-art defect detection techniques in textile manufacturing.
