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

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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.

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Authors and Affiliations

  1. Department of Chemical, Materials and Industrial Manufacturing Engineering, University of Naples Federico II, Piazzale Vincenzo Tecchio, 80125, Naples, Naples, Italy

    Giulio Mattera & Luigi Nele

  2. Notos Consulting SRL, Via Argine 313, 80147, Naples, Naples, Italy

    Davide Paolella

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  1. Giulio Mattera
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  2. Luigi Nele
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Formal analysis, GM and LN; Investigation, GM and DP; Methodology, GM and LN; Supervision, LN All authors have read and agreed to the published version of the manuscript.

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Correspondence to Giulio Mattera.

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Appendix A: A brief bliometric analysis

Appendix A: A brief bliometric analysis

In the introduction of this work some main bibliometric results are presented, which come from a brief bibliometric analysis conducted with the open-source R package bibliometric (Aria & Cuccurullo, 2017) using two different queries from Scopus website:

  • 1: WAAM OR (Wire AND Arc AND Additive AND Manufacturing)

  • 2: WAAM OR (wire AND arc AND additive AND manufacturing) OR (arc AND welding) OR GTAW OR GMAW) AND (control OR monitoring OR model) AND ((machine AND learning) OR (reinforcement AND learning) OR (artificial AND intelligence) OR (neural AND network))

Using the first query some main results are presented, such as the annual growth rate and the number of total citations. Furthermore, the top 6 keywords are presented, once a clustering activity is made limiting to the first 200 most frequent keywords. Additional information is presented in this appendix about authors and journals.

Starting from the journals the top-15 productive journals are reported in Fig. 39, and as reported in Fig. 40, The first article, written by Wang and Williams from Cranfield university UK, was published in International Journal of Advanced Manufacturing Technology, which has been the most important journal about this topic until 2018, when it was passed by Additive Manufacturing.

For what concern authors and institution, as shown in Fig. 41, 42, 43, the more productive research center, from both citation and number of produced documents, is the Wollongong University in New South Wales, Australia, and the most important authors are Pan Z and Li H. It is also fundamental to cite 2 authors Williams S. and Ding J. from Cranfield university. Just to conclude, from the conducted bibliographic analysis is possible to note that the most active author in the field of artificial intelligence application on Wire Arc Additive Manufacturing is Pan Z. and his research team at the University of Wollongong.

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Mattera, G., Nele, L. & Paolella, D. Monitoring and control the Wire Arc Additive Manufacturing process using artificial intelligence techniques: a review. J Intell Manuf 35, 467–497 (2024). https://doi.org/10.1007/s10845-023-02085-5

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