Digital Twin-Enabled Autonomous Robotic Maintenance Frameworks

Autonomous Factory Automation through Cyber-Physical Production Systems

This paper proposes a unified CPPS-based framework that integrates intelligent sensing, cyber-physical communication, distributed computing, autonomous decision support, adaptive robotic control, predictive maintenance, and real-time production optimization, and establishes CPPS as a robust foundation for sustainable, resilient, and intelligent autonomous factories.

Digital Twin-Based Performance Optimization of Industrial Robots

Industrial robots are fundamental to smart manufacturing, performing high-precision and high-speed tasks with minimal human intervention. However, maintaining optimal performance remains challenging due to equipment degradation, changing production demands, and dynamic operating conditions. Traditional maintenance approaches often fail to detect faults in real time, resulting in increased downtime, maintenance costs, and reduced productivity. Digital Twin (DT) technology addresses these challenges by creating a real-time virtual replica of industrial robots integrated with IIoT, cloud computing, edge analytics, artificial intelligence (AI), machine learning (ML), and cyber-physical systems (CPS).This study proposes a Digital Twin-Based Performance Optimization Framework that combines real-time data acquisition, AI-driven predictive analytics, virtual simulation, and adaptive control within a unified architecture. The framework enables continuous synchronization between physical robots and their virtual twins, supporting predictive maintenance, fault detection, motion optimization, and energy-efficient operation. Machine learning, deep learning, and reinforcement learning algorithms enhance anomaly detection, predictive diagnostics, and adaptive trajectory planning. A hierarchical optimization engine further improves robot kinematics, actuator performance, energy utilization, and cycle-time efficiency, while cloud-edge collaboration ensures scalable and low-latency decision-making. The proposed framework is expected to improve robot availability, predictive maintenance accuracy, energy efficiency, production throughput, and fault diagnosis while reducing operational risks and unplanned downtime. It provides a scalable foundation for intelligent, self-optimizing Industry 4.0 manufacturing systems and next-generation AI-enabled industrial robotics.

Digital twin-assisted intelligent fault diagnostics of industrial robots

Real-Time Embedded AI for Industrial Robot Monitoring

This study presents a comprehensive research framework for real-time embedded AI-based industrial robot monitoring that combines edge computing, embedded deep learning, multi-sensor fusion, anomaly detection, predictive maintenance, and intelligent decision-making and provides a scalable, energy-efficient, and intelligent monitoring solution suitable for next-generation smart factories and Industry 5.0 environments.

Artificial Intelligence-Enabled Digital Twins for Smart Manufacturing and Predictive Maintenance

Findings indicate that AI-enabled digital twins significantly improve equipment reliability, reduce unexpected failures, enhance resource utilization, and enable proactive manufacturing strategies, however, challenges related to interoperability, cybersecurity, computational complexity, data quality, and governance remain critical barriers to widespread industrial adoption.

Multi-Agent System-driven Digital Twins for predictive maintenance: architectures, technologies and open research challenges

This systematic review explores the intersection between Multi-Agent Systems and Digital Twins, with a particular focus on predictive maintenance applications in resource-constrained contexts and reveals that, despite significant progress, no existing system offers an integrated embedded-distributed hierarchical solution that simultaneously meets the requirements of Industry 5.0.