Digital Twin-Based Performance Optimization of Industrial Robots

Abstract

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.