Modernizing Textile Industry Operations with Artificial Intelligence

Background: Integrating AI technology with fundamental laws of physics has rendered the monotony of routine jobs in textile production a thing of the past. AI has been employed in studying structural and chemical properties of textile materials to ensure production efficiency and effective quality control. Methods: Literature review and several case studies on AI technology use were done focusing on three primary areas of textile production: production, fabric construction, and coloration processes. Application of AI in the industry was assessed through the use of artificial neural networks (ANN) and convolutional neural networks (CNN). Results: The study indicates significant demonstration of efficiency, speed, and reliability of AI in classifying materials, identifying defects, recognizing patterns and color detection. Intelligent systems with advanced analytics and sensors allow for more precise predictive maintenance and decision-making by providing real-time analytics and automated selection capabilities. This research on the use of artificial intelligence in textile manufacturing focuses on its application for automation, monitoring, quality control, and operational efficiency. Conclusion: This study highlights the growth potential in the AI frontier technology domain, establishing AI’s role in evolving the textile industry into a smarter, more efficient, self-operating, and sustainable industry. In fostering new solutions, it draws attention to the groundwork data and system integration shortcomings as inadequacies.