AI-Enhanced Process Optimization in Automated Production Systems

  • Authors

    • Niklaus Wirth Professor, ETH Zurich, Switzerland. Author

    DOI:

    https://doi.org/10.67228/30715725/IJIARE-2024PI4C7J

    Published 02-05-2024

  • Artificial Intelligence, Automated Production Systems, Industry 4.0, Smart Manufacturing, Machine Learning, Predictive Maintenance, Process Optimization, Industrial Internet of Things, Reinforcement Learning, Intelligent Manufacturing

    Issue

    Vol. 7 No. 1 (2024)

    Section

    Articles

    How to Cite

    [1]
    N. Wirth, “AI-Enhanced Process Optimization in Automated Production Systems”, IJIARE, vol. 7, no. 1, pp. 01–18, Feb. 2024, doi: 10.67228/30715725/IJIARE-2024PI4C7J.

  • Abstract

    Industry 4.0 integrates Artificial Intelligence (AI), Industrial Internet of Things (IIoT), cloud computing, edge computing, and cyber-physical systems to enable intelligent and automated manufacturing. Unlike traditional rule-based automation, AI-driven process optimization enables predictive decision-making, adaptive control, and continuous learning in dynamic production environments. This paper proposes an AI-enabled process optimization framework that combines real-time sensor data, predictive analytics, machine learning, reinforcement learning, optimization algorithms, and closed-loop feedback to improve manufacturing performance. The framework predicts equipment failures, detects process anomalies, optimizes production schedules, enhances resource utilization, and reduces energy consumption. Performance is evaluated using metrics such as production efficiency, cycle time, defect rate, machine utilization, predictive maintenance accuracy, throughput, energy efficiency, and operational cost. The proposed framework provides a scalable and intelligent solution for Industry 4.0 and Industry 5.0 manufacturing, improving productivity, sustainability, operational resilience, and decision-making in automated production systems.

  • References

    [1] Y. Lu, X. Xu, and L. Wang, "Digital Twin-driven smart manufacturing: Connotation, reference model, applications and research issues," Robotics and Computer-Integrated Manufacturing, vol. 71, pp. 102-115, 2021.

    [2] A. Fuller, Z. Fan, C. Day, and C. Barlow, "Digital Twin: Enabling technologies, challenges and open research," IEEE Access, vol. 8, pp. 108952-108971, 2021.

    [3] F. Tao, Q. Qi, A. Liu, and A. Kusiak, "Data-driven smart manufacturing," Journal of Manufacturing Systems, vol. 58, pp. 157-169, 2021.

    [4] A. Kusiak, "Artificial intelligence in smart manufacturing," Annual Reviews in Control, vol. 52, pp. 1-16, 2021.

    [5] S. Wang, J. Wan, D. Li, and C. Zhang, "Implementing Smart Factory of Industry 4.0: An outlook," International Journal of Distributed Sensor Networks, vol. 17, no. 2, pp. 1-15, 2021.

    [6] F. Tao and M. Zhang, "Digital Twin shop-floor: A new paradigm for smart manufacturing," IEEE Transactions on Industrial Informatics, vol. 17, no. 3, pp. 2011-2022, 2021.

    [7] Y. Liu, X. Zhang, and J. Lee, "Artificial intelligence for predictive maintenance in manufacturing systems," IEEE Access, vol. 9, pp. 109234-109248, 2021.

    [8] J. Lee, H. Davari, J. Singh, and V. Pandhare, "Industrial AI for smart manufacturing systems," Manufacturing Letters, vol. 28, pp. 40-45, 2021.

    [9] H. Lasi and P. Fettke, "AI-enabled production optimization in Industry 4.0," Computers in Industry, vol. 128, pp. 103432, 2021.

    [10] M. Grieves and J. Vickers, "Digital Twin technology in manufacturing: Recent advances and future directions," IEEE Engineering Management Review, vol. 49, no. 2, pp. 58-68, 2021.

    [11] Q. Qi and F. Tao, "Digital Twin and Big Data towards smart manufacturing," Engineering, vol. 8, pp. 18-28, 2022.

    [12] X. Xu, Y. Lu, B. Vogel-Heuser, and L. Wang, "Industry 4.0 and Industry 5.0—Inception, conception and perception," Journal of Manufacturing Systems, vol. 61, pp. 530-535, 2022.

    [13] C. Zhang, Y. Zhou, and J. Wan, "Machine learning-enabled process optimization for intelligent manufacturing," IEEE Access, vol. 10, pp. 14235-14249, 2022.

    [14] H. Wang, Z. Zhang, and F. Tao, "Deep learning approaches for industrial process optimization," Robotics and Computer-Integrated Manufacturing, vol. 73, pp. 102247, 2022.

    [15] X. Chen, Y. Li, and J. Wu, "Predictive analytics in Industry 4.0 manufacturing environments," IEEE Transactions on Industrial Informatics, vol. 18, no. 5, pp. 3201-3213, 2022.

  • Downloads

Similar Articles

71-80 of 84

You may also start an advanced similarity search for this article.