Analysing the Impact of Machine Learning on Textile Quality Enhancement and Defect Detection

Abstract

One of the mainstays of global manufacturing is the textile sector, where quality control is essential to guaranteeing both customer happiness and product dependability. Conventional techniques for identifying flaws in fabric textures and improving quality frequently depend on manual examination, which is laborious, arbitrary, and prone to mistakes. This study examines the revolutionary effects of machine learning (ML) in improving textile quality and identifying flaws. Machine learning (ML) provides accurate, automated, and scalable methods for detecting anomalies in fabric textures and enhancing manufacturing efficiency by utilizing sophisticated algorithms, such as supervised learning, unsupervised learning, and deep learning approaches. This paper highlights the effective use of ML models in the textile industry by reviewing current practices and investigating ML applications in pattern recognition, anomaly detection, and predictive maintenance. There is also discussion of difficulties including integration into conventional procedures, computing complexity, and data restrictions. The results highlight how machine learning (ML) has the potential to transform the textile industry by lowering faults, streamlining procedures, and spurring innovation in quality control systems. This study ends with suggestions for future developments, such as new technologies and cooperative strategies to strengthen machine learning's position in the textile sector.