Autonomous Factory Automation through Cyber-Physical Production Systems

  • Authors

    • Michael Rabin Professor, Hebrew University of Jerusalem, Israel Author
    • Amir Pnueli Professor, Weizmann Institute of Science, Israel Author

    DOI:

    https://doi.org/10.67228/30715725/IJIARE-2023PII9H4F

    Published 12-04-2023

  • Autonomous Factory Automation, Cyber-Physical Production Systems, Industry 4.0, Smart Manufacturing, Industrial Internet Of Things, Artificial Intelligence, Edge Computing, Digital Twin, Industrial Robotics, Predictive Maintenance

    Issue

    Vol. 6 No. 2 (2023)

    Section

    Articles

    How to Cite

    [1]
    M. Rabin and A. Pnueli, “Autonomous Factory Automation through Cyber-Physical Production Systems”, IJIARE, vol. 6, no. 2, pp. 01–17, Dec. 2023, doi: 10.67228/30715725/IJIARE-2023PII9H4F.

  • Abstract

    The rapid advancement of Industry 4.0 has accelerated the adoption of intelligent automation technologies that enhance productivity, flexibility, quality, and operational resilience in manufacturing. Cyber-Physical Production Systems (CPPS) have emerged as a key enabler of smart manufacturing by integrating physical production equipment with Artificial Intelligence (AI), Industrial Internet of Things (IIoT), cloud and edge computing, digital twins, and autonomous control. Unlike traditional centralized automation, CPPS enables decentralized communication, real-time data exchange, intelligent decision-making, and adaptive production control. Autonomous factory automation allows machines, robots, and sensors to monitor operating conditions, predict equipment failures, and perform corrective actions with minimal human intervention. AI techniques such as machine learning, deep learning, reinforcement learning, and predictive analytics optimize production scheduling, resource allocation, predictive maintenance, and operational efficiency. Meanwhile, IIoT sensors, edge computing, and cloud platforms provide continuous monitoring, low-latency processing, and enterprise-level data analytics.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. The framework enables manufacturing systems to dynamically respond to equipment failures, production disturbances, and changing customer demands while maintaining operational stability and product quality. Mathematical models for production optimization and resource allocation demonstrate improvements in throughput, equipment utilization, energy efficiency, and production time. Overall, the proposed framework establishes CPPS as a robust foundation for sustainable, resilient, and intelligent autonomous factories, supporting next-generation smart manufacturing through the seamless integration of AI, IIoT, and cyber-physical engineering.

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