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Optimal data-driven control of manufacturing processes using reinforcement learning: an application to wire arc additive manufacturing

Author

Listed:
  • Giulio Mattera

    (University of Naples Federico II)

  • Alessandra Caggiano

    (University of Naples Federico II
    Fraunhofer Joint Laboratory of Excellence on Advanced Production Technology (FhJ_LEAPT UniNaples))

  • Luigi Nele

    (University of Naples Federico II)

Abstract

Nowadays, artificial intelligence (AI) has become a crucial Key Enabling Technology with extensive application in diverse industrial sectors. Recently, considerable focus has been directed towards utilizing AI for the development of optimal control in industrial processes. In particular, reinforcement learning (RL) techniques have made significant advancements, enabling their application to data-driven problem-solving for the control of complex systems. Since industrial manufacturing processes can be treated as MIMO non-linear systems, RL can be used to develop complex data-driven intelligent decision-making or control systems. In this work, the workflow for developing a RL application for industrial manufacturing processes, including reward function setup, development of reduced order models and control policy construction, is addressed, and a new process-based reward function is proposed. To showcase the proposed approach, a case study is developed with reference to a wire arc additive manufacturing (WAAM) process. Based on experimental tests, a Reduced Order Model of the system is obtained and a Deep Deterministic Policy Gradient Controller is trained with aim to produce a simple geometry. Particular attention is given to the sim-to-real process by developing a WAAM simulator which allows to simulate the process in a realistic environment and to generate the code to be deployed on the motion platform controller.

Suggested Citation

  • Giulio Mattera & Alessandra Caggiano & Luigi Nele, 2025. "Optimal data-driven control of manufacturing processes using reinforcement learning: an application to wire arc additive manufacturing," Journal of Intelligent Manufacturing, Springer, vol. 36(2), pages 1291-1310, February.
    • Handle: RePEc:spr:joinma:v:36:y:2025:i:2:d:10.1007_s10845-023-02307-w
    DOI: 10.1007/s10845-023-02307-w
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    References listed on IDEAS

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    1. Lu Liu & Siyuan Tian & Dingyu Xue & Tao Zhang & YangQuan Chen, 2019. "Industrial feedforward control technology: a review," Journal of Intelligent Manufacturing, Springer, vol. 30(8), pages 2819-2833, December.
    2. Volodymyr Mnih & Koray Kavukcuoglu & David Silver & Andrei A. Rusu & Joel Veness & Marc G. Bellemare & Alex Graves & Martin Riedmiller & Andreas K. Fidjeland & Georg Ostrovski & Stig Petersen & Charle, 2015. "Human-level control through deep reinforcement learning," Nature, Nature, vol. 518(7540), pages 529-533, February.
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