Digital twins create precise, dynamic, real-time replicas of physical assets, processes, and systems. Asset twins monitor equipment continuously and proactively, process twins optimize production flows efficiently, and product twins accelerate design iterations significantly. While integration challenges persist, forward-thinking manufacturers report consistent gains of 15-25% in predictive maintenance accuracy.

Artificial intelligence is transforming maintenance by enabling smarter, predictive strategies that prevent equipment failures before they occur. This study investigates the integration of AI into maintenance workflows to enhance reliability, minimize unplanned downtime, and optimize resource utilization. The findings highlight AI’s role in creating more efficient, cost-effective operations through timely, data-driven decision-making.

Artificial Intelligence is reshaping optical communication networks by enhancing their intelligence, reliability, and efficiency. This study examines how AI-driven algorithms and machine learning models are used to predict potential issues, optimize traffic flow, detect faults in real time, and strengthen network security. These advancements are transforming optical networks into smarter, more resilient systems that can meet growing connectivity demands.