Author(s)

Jing Pu ¹

Affiliation(s)

¹ Xihua University, Chengdu, Sichuan, 610039, China

Corresponding Author

Jing Pu

ABSTRACT

Digital Twin (DT) technology represents a paradigm shift in industrial automation, enabling the creation of a dynamic, virtual replica of a physical system that can simulate, analyze, and control its real-world counterpart. This paper presents a comprehensive framework for the development, implementation, and validation of a high-fidelity Digital Twin for a modular automated assembly line. The assembly line comprises several interconnected workstations, including a robotic pick-and-place unit, an automated screwing station, a vision-based quality inspection module, and a conveyor-based transport system. The primary objective is to demonstrate how a synchronised Digital Twin can enhance operational efficiency, facilitate predictive maintenance, and provide a robust platform for virtual commissioning and operator training. The proposed methodology integrates physics-based multi-domain modelling for mechanical and control systems with real-time data ingestion from industrial IoT sensors (encoders, force-torque sensors, vision cameras) via an OPC UA communication architecture. The Digital Twin is realized using a co-simulation platform that synchronizes a 3D discrete-event simulation environment with real-time PLC code and data analytics algorithms. The results, derived from a simulated operational cycle equivalent to one week of continuous production, show a significant improvement in overall equipment effectiveness (OEE) by approximately 18.7%, a reduction in unplanned downtime by 35%, and an increase in first-pass yield by 12.3%. Furthermore, the Digital Twin successfully predicted two potential failures in the robotic gripper mechanism 48-72 hours before performance degradation crossed operational thresholds. The discussion elaborates on the implications of these findings for lifecycle management and the challenges of model fidelity and data integration. This research confirms the substantial potential of Digital Twins as a cornerstone for the realization of smart, adaptive, and resilient manufacturing systems.

KEYWORDS

Digital Twin; Cyber-Physical System; Predictive Maintenance; Overall Equipment Effectiveness; Real-Time Synchronization; Prognostic Health Management; IoT Integration; Industrial Automation

CITE THIS PAPER

Jing Pu. Digital Twin Modeling and Simulation for an Automated Assembly Line. Journal of Engineering Mechanics and Machinery (2026). Vol. 11, No. 1, 1-7. DOI: http://dx.doi.org/10.23977/jemm.2026.110101.

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Sponsors, Associates, and Links

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• Cybernetics and Mechatronics
  
  
• Digital Manufacturing and Process Management
  
  
• Ultra-Precision Machining Process
  
  
• Journal of Robotics and Biomimetics
  
  
• Prognostics, Diagnostics and Health Management
  
  
• Micro-Electro-Mechanical Systems
  
  
• Journal of Precision Instrument and Machinery
  
  
• Engineering and Solid Mechanics
  
  
• Fracture and Damage Mechanics
  
  
• Frontiers in Tribology
  
  
• Fluid and Power Machinery
  
  
• Chemical Process Equipment
  
  
• Journal of Assembly and Manufacturing
  
  
• Mechanical Vibration and Noise