Publication

Abstract : This study explores the integration of computer vision (CV) techniques for machine learning development of digital twins for manufacturing applications. Digital twins represent a transformative approach to process monitoring and optimization in a field prone to uncertainties and operational inefficiencies. However, existing implementations using sensors for event modelling often encounter limitations in scalability and compatibility, especially with legacy machines, and in monitoring non-machine events such as maintenance events. The research aims to use computer vision as a tool to overcome these challenges. The proposed approach employs computer vision-based learning models to capture and analyse real-time data from physical processes; both machine and non-machine events, addressing the constraints of traditional digital twin systems and providing a more comprehensive and non-invasive monitoring. This approach enhances the adaptability and utility of digital twins, making them low-cost, more scalable, accurate, and responsive to user needs in complex manufacturing environments.