Abstract: The garment industry is experiencing a transformative shift by integrating intelligent learning technologies, including machine learning (ML) and deep learning. This applied research with case study examines the application of Convolutional Neural Networks (CNNs) for automating quality control in garment manufacturing, specifically focusing on the detection of sewing lines in captured images. Manufacturers can enhance efficiency, improve product quality, and reduce operational costs by utilizing advanced data analytics and image processing techniques. The CNN model is trained to identify unique features of sewing lines, allowing for real-time comparisons between machine outputs and standard benchmarks. This intelligent learning approach not only streamlines the inspection process but also enables predictive maintenance and data-driven decision-making, fostering adaptability to market demands. The findings highlight the potential of AI-driven solutions to replace manual inspections, ultimately driving innovation and sustainability in garment production. As the industry evolves, embracing intelligent learning technologies will be crucial for manufacturers seeking to maintain a competitive edge in an increasingly dynamic marketplace. This research underscores the importance of preparing high-quality training data to optimize CNN performance and ensure effective garment quality control.
Keywords: Artificial Intelligence, AI, Automation, Convolutional Neural Networks, Machine Learning, Garment Manufacturing, Quality Control.
Title: AI Intelligent learning for Manufacturing Automation
Author: Ray Wai Man Kong, Ding Ning, Theodore Ho Tin Kong
International Journal of Mechanical and Industrial Technology
ISSN 2348-7593 (Online)
Vol. 13, Issue 1, April 2025 - September 2025
Page No: 1-9
Research Publish Journals
Website: www.researchpublish.com
Published Date: 05-April-2025
