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Predicting porosity in wire arc additive manufacturing (WAAM) using wavelet scattering networks and sparse principal component analysis

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Abstract

Wire arc additive manufacturing (WAAM) is getting much research attention because of its cost-effectiveness in the metallic production of large and complex parts. In pursuit of best-quality products and minimizing material loss, multimodal process monitoring methods are key. This paper presents the use of acoustic and current signals in identifying one of the critical defects in WAAM, i.e., porosity. Aluminum and unalloyed steel were deposited in a controlled environment which developed different amounts of porosity alongside measurements from current and gas sensors. Feature reduction of the signals was carried out using a combination of wavelet scattering networks and sparse principal component analysis (sPCA). While the models predict porosity reasonably, the dominant features learned by the model are also investigated and reported.

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Data availability

The datasets used and/or analyzed during this study are available from the corresponding author upon reasonable request.

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Acknowledgements

The authors would like to thank the KU Leuven MaPS AM group for lending their high-frequency AE sensor.

Funding

The project has received funding from KU Leuven grant STG/19/047 and was partially supported by Flanders Make, the strategic research center for the manufacturing industry, via the MuSIC_SBO project.

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Authors and Affiliations

  1. Department of Mechanical Engineering, KU Leuven, Jan Pieter de Nayerlaan 5, 2860, Sint-Katelijne-Waver, Belgium

    Joselito Yam Alcaraz II & Tegoeh Tjahjowidodo

  2. Flanders Make @KU Leuven, KU Leuven, Celestijnenlaan 300, 3001, Leuven, Belgium

    Joselito Yam Alcaraz II & Tegoeh Tjahjowidodo

  3. Department of Materials Engineering, KU Leuven, Jan Pieter de Nayerlaan 5, 2860, Sint-Katelijne-Waver, Belgium

    Abhay Sharma

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  1. Joselito Yam Alcaraz II
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Correspondence to Joselito Yam Alcaraz II or Tegoeh Tjahjowidodo.

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Alcaraz, J., Sharma, A. & Tjahjowidodo, T. Predicting porosity in wire arc additive manufacturing (WAAM) using wavelet scattering networks and sparse principal component analysis. Weld World 68, 843–853 (2024). https://doi.org/10.1007/s40194-024-01709-5

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  • DOI: https://doi.org/10.1007/s40194-024-01709-5

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