DIGITAL TWIN-BASED PREDICTIVE MAINTENANCE OF SMART MANUFACTURING EQUIPMENT

Abstract

The increasing use of automation, connected machines, intelligent sensors, and data analytics has changed the way manufacturing equipment is operated and maintained. Conventional corrective and scheduled maintenance strategies often result in unexpected machine failures, unnecessary component replacement, production interruption, and increased maintenance expenditure. Digital Twin technology provides an alternative approach by creating a continuously updated virtual representation of physical manufacturing equipment. This study investigates the application of a Digital Twin-based predictive maintenance framework for monitoring the health of smart manufacturing equipment and identifying developing faults before functional failure occurs. The study further identifies data quality, model accuracy, computational requirements, cybersecurity, and integration with existing manufacturing systems as important considerations for industrial implementation. The proposed framework demonstrates how Digital Twin technology can support reliable and intelligent maintenance decision-making in modern smart manufacturing environments.