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Monitoring and control the Wire Arc Additive Manufacturing process using artificial intelligence techniques: a review

Author

Listed:
  • Giulio Mattera

    (University of Naples Federico II)

  • Luigi Nele

    (University of Naples Federico II)

  • Davide Paolella

    (Notos Consulting SRL)

Abstract

Wire Arc Additive Manufacturing is a Direct Energy Deposition additive technology that uses the principle of wire welding to deposit layers of material to create a finished component. This technology is finding an increasing interest in the manufacturing industry, especially thanks the low cost and the possibility to build large-scale components. Nowadays, the boosting to transition into smart manufacturing systems and the increasingly computational resources allowed the development of intelligent applications for smart production systems for both in situ inspection and process parameter control. This paper aims to provide an review of applications developed using artificial intelligence techniques for Wire Arc Additive Manufacturing, with particular focus on defect detection software modules, feedback generation for control system and innovative control strategies as reinforcement learning to overcome problems related to model non-linearity and uncertainties.

Suggested Citation

  • Giulio Mattera & Luigi Nele & Davide Paolella, 2024. "Monitoring and control the Wire Arc Additive Manufacturing process using artificial intelligence techniques: a review," Journal of Intelligent Manufacturing, Springer, vol. 35(2), pages 467-497, February.
    • Handle: RePEc:spr:joinma:v:35:y:2024:i:2:d:10.1007_s10845-023-02085-5
    DOI: 10.1007/s10845-023-02085-5
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    References listed on IDEAS

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    1. Aria, Massimo & Cuccurullo, Corrado, 2017. "bibliometrix: An R-tool for comprehensive science mapping analysis," Journal of Informetrics, Elsevier, vol. 11(4), pages 959-975.
    2. Chunyang Xia & Zengxi Pan & Joseph Polden & Huijun Li & Yanling Xu & Shanben Chen, 2022. "Modelling and prediction of surface roughness in wire arc additive manufacturing using machine learning," Journal of Intelligent Manufacturing, Springer, vol. 33(5), pages 1467-1482, June.
    3. Haochen Mu & Joseph Polden & Yuxing Li & Fengyang He & Chunyang Xia & Zengxi Pan, 2022. "Layer-by-layer model-based adaptive control for wire arc additive manufacturing of thin-wall structures," Journal of Intelligent Manufacturing, Springer, vol. 33(4), pages 1165-1180, April.
    4. Richard Bellman, 1954. "On some applications of the theory of dynamic programming to logistics," Naval Research Logistics Quarterly, John Wiley & Sons, vol. 1(2), pages 141-153, June.
    5. 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.
    6. David Silver & Aja Huang & Chris J. Maddison & Arthur Guez & Laurent Sifre & George van den Driessche & Julian Schrittwieser & Ioannis Antonoglou & Veda Panneershelvam & Marc Lanctot & Sander Dieleman, 2016. "Mastering the game of Go with deep neural networks and tree search," Nature, Nature, vol. 529(7587), pages 484-489, January.
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    Cited by:

    1. Mario Vozza & Joseph Polden & Giulio Mattera & Gianfranco Piscopo & Silvestro Vespoli & Luigi Nele, 2024. "Explaining the Anomaly Detection in Additive Manufacturing via Boosting Models and Frequency Analysis," Mathematics, MDPI, vol. 12(21), pages 1-17, October.

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