Impact of Digital Twin Technology on Knowledge-Driven Manufacturing Systems

Abstract

The emergence of new digital technologies has profoundly changed contemporary manufacturing, with Digital Twin
(DT) technology serving as a foundation of Industry 4.0. This paper analyzes the effects of Digital Twin technology
on knowledge-driven manufacturing systems through operational efficiency, real-time decision making, predictive
maintenance, and knowledge synthesis to gauge its impact. The research adopts a techno-functional modeling
approach by applying DT frameworks in a simulated smart manufacturing environment that mirrors actual shop floor
activities. The data generated from digital twin simulations was benchmarked against traditional process control
systems to evaluate enhancements in system responsiveness, fault prediction, and knowledge dissemination. The
outcomes demonstrate the substantial gains in productivity achieved through the use of DT-enabled manufacturing
systems, which also reported a 30% reduction in downtime and increased knowledge sharing within the production
ecosystem. The methodology also shows the role of DT technology in developing a closed loop feedback system with
proactive revisions to be made on design, production, and maintenance activities. The results highlight the significant
impact of Digital Twins on the level of intelligence, flexibility, and automation incorporated in manufacturing
workflows. This paper posits conclusions with recommendations on the integration of AI, IoT, and cloud technology
into DT systems towards next-generation knowledge-based resilient adaptable data-driven manufacturing systems.