A review of the evolution and concepts of deep learning and AI in the textile industry

Machine learning (ML) and deep learning (DL) are transforming the textile industry by integrating advanced technologies into various processes. Textiles, once seen as passive materials, are now essential components of complex systems due to automation and innovative materials. This review focuses on articles that utilized AI, ML, or DL in textile research and industry. The review presents bibliometric analysis of AI methods in textiles. Later, the review is structured into sections that examine the effect of ML and DL across the textile sector. We outline key ML and DL methods applied in textiles, discussing their main uses and potential applications. This overview aims to clarify the working principles behind these methods, which are explored in greater detail. The methods analyzed range from basic linear regression to ensemble techniques such as XGBoost. DL techniques include convolutional neural networks for image analysis and long short-term memory networks for time-series analysis. In addition, a bibliometric review identifies trends and gaps in the literature, highlighting areas for future research. We also provide a detailed examination of how these methods are implemented in textiles.