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Real-time prediction of deposited bead width in L-DED using semi-supervised transfer learning

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Abstract

Laser directed energy deposition (L-DED) is an additive manufacturing (AM) technology that offers unique advantages for creating and repairing metallic parts. The quality of the final print is highly dependent on the consistency of the printing parameters, and as a result, the deposited bead geometry along the printing pathway. Inconsistencies, particularly in the bead width, can produce defects between adjacent beads, increasing the material susceptibility to failure. Therefore, it is crucial to monitor and predict the printing parameters in real time during the process. The objective of this study is to develop a machine learning model that accurately predicts the width of the deposited track in real time. To achieve this, a dataset with approximately 12,379 melt pool images was used to train the algorithm and predict the width of the deposited track. Each one of the deposited track was measured by a profilometer at 10 points along the pathway, and intermediate labels were predicted using a spline curve, making the model semi-supervised. To implement the machine learning model, pre-trained frozen networks based on convolutional neural networks (CNN) architectures VGG, ResNet, and DenseNet were employed, using transfer learning principles. These networks were integrated into a dense, fully connected network containing trainable parameters. The results demonstrate a good model fit, with a mean absolute error of 4.5% and a mean absolute error of 0.0358 mm. Moreover, the processing frequency of the best model, at 55 Hz, enables real-time control of the L-DED manufacturing process. It is important to highlight, however, that the VGG-based model shows a frequency of 250 Hz and similar results. Thus, accurately predicting the width of the deposited bead has the potential to significantly improve the quality of the final part by reducing overall defects, such as lack of fusion and porosity, whenever this data is used as input to a feedback control system. This is particularly valuable in industries where mechanical properties and fatigue life are critical, such as automotive, aerospace, and biomedical sectors. The models developed in this study offer a promising approach to effectively provide data to control the L-DED manufacturing process in real time, enabling the reliable production of high-quality components associated with reduced manufacturing costs.

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Availability of data and materials

The data collected has been made available to support future research and training of algorithms and is available at github.com/henriquenunez/nir-ded-dataset.

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Acknowledgements

The authors are thankful to Tatiana Dias Santana, from Mahr do Brasil, for aiding this study with accurate measurements of the workpiece. This work was fully supported by grant #2016/11309 – 0, #2019/00343 – 1 and #2020/03119 – 2, São Paulo Research Foundation (FAPESP).

Funding

Grant #2016/11309–0 and #2019/00343–1, São Paulo Research Foundation (FAPESP).

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Author notes

  1. Henrique H. L. Núñez, Kandice S. B. Ribeiro, and Giuliana S. Venter contributed equally to this work.

Authors and Affiliations

  1. Department of Mechanical Engineering, Technology Sector, Federal University of Parana, Curitiba, 81530-000, PR, Brazil

    Victor H. Mochi & Giuliana S. Venter

  2. Department of Computer Science, Institute of Mathematics and Computer Science, University of São Paulo, São Carlos, 13566-590, SP, Brazil

    Henrique H. L. Núñez

  3. Department of Mechanical and Materials Engineering, University of Turku, Turku, 20540, Finland

    Kandice S. B. Ribeiro

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  1. Victor H. Mochi
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Contributions

Victor H. Mochi contributed to the study methodology, software, investigation and analysis. Kandice S. B. Ribeiro contributed to the study conceptualization, methodology and experiment investigation. Henrique H. L. and Giuliana S. Venter aided the conceptualization and methodology for both the experiments and software. Data curation and writing were performed by Victor H. Mochi, Giuliana S. Venter and Kandice S. B. Ribeiro. Reginaldo T. Coelho contribution was related to the research resources. All authors read and approved the final manuscript.

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Correspondence to Giuliana S. Venter.

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Mochi, V.H., Núñez, H.H.L., Ribeiro, K.S.B. et al. Real-time prediction of deposited bead width in L-DED using semi-supervised transfer learning. Int J Adv Manuf Technol 129, 5643–5654 (2023). https://doi.org/10.1007/s00170-023-12658-6

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